Systems and methods to track locations visited by mobile devices and determine neighbors of and distances among locations

ABSTRACT

Systems and methods including mobile devices determining their locations using a location determination system, such as a global positioning system. A set of locations, including locations of one or more mobile devices, are identified by their coordinates on the surface of the Earth. The set of locations are efficiently organized into a graph of locations connecting to neighboring locations with edges representing distances to their neighboring locations. For each respective location, a computing device combines coordinates of the respective location into an identifier of a cell that contains the respective location without floating point computations, and stores cell-location data associating respective cells with respective locations. For each respective location, the computing device identifies neighboring cells of the cell that contains the respective location, looks up locations associated with the identifiers of the cell and its neighboring cells, as neighboring locations or candidates for neighboring locations.

RELATED APPLICATIONS

The present application claims the benefit of the filing dates of Prov.U.S. Pat. App. Ser. No. 62/377,256, filed Aug. 19, 2016 and entitled“Systems and Methods to Track Locations Visited by Mobile Devices andDetermine Neighbors of and Distances among Locations”, and Prov. U.S.Pat. App. Ser. No. 62/346,689, filed Jun. 7, 2016 and entitled “Systemsand Methods to Track Regions Visited by Mobile Devices and DetectChanges in Location Patterns”, the entire disclosures of whichapplications are hereby incorporated herein by reference.

The present application relates to U.S. patent application Ser. No.14/593,947, filed Jan. 9, 2015 and issued as U.S. Pat. No. 9,307,360 onApr. 5, 2016, which has a continuation U.S. patent application Ser. No.15/016,067, filed Feb. 4, 2016 and published as U.S. Pat. App. Pub. No.2016/0205503. The present application also relates to U.S. Pat. App.Pub. Nos. 2014/0012806, 2015/0052132, and U.S. Pat. Nos. 9,291,700 and9,374,671. The entire disclosures of the above identified patents and/orpatent applications are hereby incorporated herein by reference.

FIELD OF THE TECHNOLOGY

At least one embodiment of the disclosure relates to computationalefficient ways to identify neighboring locations and compute distancesamong neighboring locations.

BACKGROUND

A location determination system, such as a Global Positioning System(GPS), allows a mobile device, such as a mobile phone, a smart phone, apersonal media player, a GPS receiver, etc., to determine its currentlocation on the earth. The location of the mobile device is typicallycalculated as a set of coordinates, such as the longitude and latitudecoordinates of a point on the surface of the earth.

However, the location of the mobile device in the form of coordinates ofa point on the surface of the earth does not provide sufficientinformation of interest about the location, such as whether the mobiledevice is within a particular region associated with a set of knownproperties.

For example, it may be of interest in certain applications to determinewhether the location of the mobile device is within the store of amerchant, within the home of the user of the mobile device, within arecreation area, within a commercial district, etc.

For example, U.S. Pat. App. Pub. No. 2014/0012806, published Jan. 9,2014 and entitled “Location Graph Based Derivation of Attributes”,discusses the generation of a user profile based on mapping thelocations of a mobile device to predefined geographical regions and usethe attributes associated with the predefined geographical regions toderive and/or augment the attributes of the user profile.

For example, U.S. Pat. App. Pub. No. 2008/0248815, published Oct. 9,2008 and entitled “Systems and Methods to Target Predictive Locationbased Content and Track Conversions”, discusses the need to analyze thelocation of a mobile device to determine the types of businesses thatthe user of the mobile device typically visits, or visited. When thelocation of a mobile device is within a predefined distance from eitherthe address of a particular business or a geographic location associatedwith the business, or within a geometric perimeter of the particularbusiness location, it may be determined that the user of the mobiledevice was at the particular business.

Ray Casting is a known technology to determine whether a given point iswithin a polygon represented by a set of vertexes. However, Ray Castingis computational intensive involving floating point number computations.

The Military Grid Reference System (MGRS) is a standard used forlocating points on the earth. It uses grid squares of various lengths atdifferent resolutions, such as 10 km, 1 km, 100 m, 10 m, or 1 m,depending on the precision of the coordinates provided. An MGRScoordinate includes a numerical location within a 100,000 meter square,specified as n+n digits, where the first n digits give the easting inmeters, and the second n digits give the northing in meters.

The disclosures of the above discussed patent documents are herebyincorporated herein by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings in which like referencesindicate similar elements.

FIG. 1 shows a system to determine whether a mobile device is within aregion having a predetermined geographical boundary according to oneembodiment.

FIGS. 2-4 illustrate a grid system used to determine whether a locationof a mobile device is within the geographical boundary of a regionaccording to one embodiment.

FIGS. 5-7 illustrate a hierarchical grid system used to determinewhether a location of a mobile device is within the geographicalboundary of a region according to one embodiment.

FIGS. 8 and 9 show a top level grid and the identification of cellswithin the grid according to one embodiment.

FIG. 10 shows an intermediate level grid and the identification of cellswithin the grid according to one embodiment.

FIG. 11 shows the identification of cells within a grid having thefinest resolution in a grid hierarchy according to one embodiment.

FIG. 12 shows the method to determine whether a location of a mobiledevice is within the geographical boundary of a region according to oneembodiment.

FIG. 13 illustrates an example of converting the coordinates of alocation to an identifier of a cell and converting the identifier of thecell to the coordinates of a vertex of the cell according to oneembodiment.

FIG. 14 shows a system configured to map a location of a mobile deviceto one or more identifications of regions according to one embodiment.

FIG. 15 illustrates a data processing system according to oneembodiment.

FIG. 16 shows a method of mapping a location of a mobile device to aregion according to one embodiment.

FIG. 17 shows a method to detect differences in location patternsaccording to one embodiment.

FIG. 18 shows a method to detect differences in location patterns ofdifferent mobile devices visiting a predetermined region according toone embodiment.

FIG. 19 shows a method to measure the influence of an event based ondifferences in location patterns of mobile devices visiting apredetermined region according to one embodiment.

FIG. 20 shows a method to identify mobile devices having similarpatterns of visiting a predetermined region according to one embodiment.

FIG. 21 shows a method to identify mobile devices having similarpatterns of visiting predetermined regions according to one embodiment.

FIG. 22 shows a method to measure the influence of an event according toone embodiment.

FIGS. 23-25 illustrate a system to organize location data via a gridsystem according to one embodiment.

FIG. 26 illustrates a location data processing system to establish agraph of locations according to one embodiment.

FIG. 27 shows a method to generate a location graph according to oneembodiment.

DETAILED DESCRIPTION

The following description and drawings are illustrative and are not tobe construed as limiting. Numerous specific details are described toprovide a thorough understanding. However, in certain instances, wellknown or conventional details are not described in order to avoidobscuring the description. References to one or an embodiment in thepresent disclosure are not necessarily references to the sameembodiment; and, such references mean at least one.

One embodiment of the disclosure provides a computationally efficientmethod and system to determine whether a location of the mobile deviceis within a predetermined geographical boundary of a region and/or todetermine, among a plurality of predefined regions, the identity of oneor more regions within which the location of the mobile device ispositioned.

FIG. 1 shows a system to determine whether a mobile device is within aregion having a predetermined geographical boundary according to oneembodiment.

In FIG. 1, a location determination system uses the wireless signals(e.g., 179) transmitted to and/or from the mobile device (109) todetermine the location (111) of the mobile device (109) on the surfaceof the earth.

For example, the location determination system may use GlobalPositioning System (GPS) satellites (e.g., 117) (and/or base stations(e.g., 115)) to provide GPS signals to the mobile device (109). Themobile device (109) is configured to determine the location (111) of themobile device (109) based on the received GPS signals. In general,multiple GPS satellites (e.g., 117) and/or base stations (e.g., 115) areused to provide the wireless signals (e.g., 179) from differentlocations for a GPS receiver to determine its locations.

In FIG. 1, the mobile device (109) is configured with a cellularcommunications transceiver to communicate with the base stations (e.g.,113, 115) of a cellular communications network.

For example, in one embodiment, the mobile device (109) is configured touse signal delays in the cellular communications signals to or from aplurality of cellular base stations (e.g., 113, . . . , 115) to computethe location coordinates of the mobile device (109).

In FIG. 1, a server (187) is configured to communicate with the mobiledevice (109) via the network (189) and the cellular communicationsinfrastructure (e.g., the base station (113)). The server (187) isconnected to a database (181) storing information about the predefinedregions (e.g., 101, 103, . . . 105, 107).

For example, the database (181) is configured to store theidentifications of a set of cells that are within the boundary of aregion (e.g., 101). The server (187) is configured to convert thelocation (111) of the mobile device (109) to a cell identification andsearch the identifications of the set of cells representing the region(101) to determine if the cell identification converted from thelocation (111) of the mobile device (109) is in the set of cellidentifications representing the region (101). If the cellidentification of the location (111) is found in the set of cellidentifications representing the region (101), the location (111) isconsidered being within the boundary of the region (e.g., 101).

In one embodiment, a hierarchical grid system is used to construct cellsthat are within the boundary of the region (e.g., 101). Thus, the numberof cells within the region (e.g., 101) can be reduced, while theprecision of the determination can be selected at a desired level (e.g.,1 meter).

In one embodiment, the identifications of the cells are configured to besigned integer numbers. Thus, any known technologies for searching agiven number within a set of signed integer numbers can be used toefficiently determine whether the cell identifier of a location (111) iswithin the set of cell identifiers of the region (101).

In one embodiment, the conversion of the location coordinates to a cellidentifier is configured for improved computation efficiency. The cellidentifier is also configured for efficient determination of theresolution of the grid in which the cell is located, the coordinates ofthe vertexes of the cell, the bounding boxes of the cell, and theidentifications of the neighbors of the cells. Details and examples areprovided below.

In one embodiment, a given region (e.g., polygon) on earth isrepresented by a set of cells in a hierarchical, regular grid in alongitude latitude space. In the longitude latitude space, the cells areuniform rectangles/squares at a given resolution; the cell identifiesare constructed from the digits of the longitude/latitude coordinatesfor improved efficiency in conversion between coordinates and cellidentifiers. In one embodiment, the resolution levels of the gridscorrespond to the precision of the longitude/latitude coordinates interms of the number of digits used to after the decimal point torepresent the longitude/latitude coordinates.

At a given resolution in the grid, the identity of the cell thatcontains a given point identified by a longitude/latitude pair can becomputed via simple manipulations of the digits of thelongitude/latitude pair, as illustrated in FIG. 13.

FIGS. 2-4 illustrate a grid system used to determine whether a locationof a mobile device is within the geographical boundary of a regionaccording to one embodiment.

In FIG. 2, a grid (121) of cells is used to identify an approximation ofthe region (101) at a given level of resolution of the grid (121). Theresolution level corresponds to the size of the cells in the grid (121).

In FIG. 2, the region (101) is represented as a polygon having a set ofvertexes (e.g., 123). The set of line segments connecting theneighboring vertexes (e.g., 123) of the region (101) defines theboundary of the region (101).

FIG. 3 illustrates the selection of a set of cells (e.g., 127) that areconsidered to be within the boundary of the region (101). Variousdifferent methods and/or criteria can be used to classify whether a cellis within the boundary of the region (101), especially the cells thatare partially in the region (101) and contain a portion of the boundaryof the region (101). The disclosure of the present application is notlimited to a particular way to identify or classify whether a cell thatis within the boundary of the region (101).

For example, a cell may be classified as being with the region (101)when the overlapping common portion between the cell and the region(101) is above a predetermined percentage of the area of the cell.

For example, a cell may be classified as being with the region (101)when a length of one or more segments of the region (101) going throughthe cell is above a threshold.

For example, the vertexes of the region (101) may be mapped to thenearest grid points to determine an approximation of the boundary of theregion (101) that aligns with the grid lines to select the cells thatare located within the approximated boundary of the region (101).

FIG. 4 illustrates the determination of the location (111) within theset of cells (131, . . . , 133, . . . , 139) according to oneembodiment. In FIG. 4, each of the cells (131, . . . , 133, . . . , 139)represents a portion of the region (101). To determine whether thelocation (111) is within the boundary of the region (101), the system isconfigured to determine whether the set of cells (131, . . . , 133, . .. , 139) contains the location (111).

In one embodiment, to efficiently determine whether any of the cells(131, . . . , 133, . . . , 139) contains the location (111), each of thecells (131, . . . , 133, . . . , 139) is assigned a cell identifier. Inone embodiment, each of the cell identifier is a signed integer forimproved computation efficiency; and the cell identifier is configuredin such a way that the coordinates of any location within the cell canbe manipulated via a set of predetermined, computationally efficientrules to provide the same cell identifier, as further illustrated inFIGS. 12 and 13.

In FIG. 4, after the coordinates of the location (111) is converted tothe cell identifier of the cell (133) that contains the location (111),the system determines whether the location (111) is within the regioncorresponding to the set of cells (131, . . . , 133, . . . , 139) bysearching in the cell identifiers of the set of cells (131, . . . , 133,. . . , 139) representative of the region (101) to find a match to thecell identifier of the cell (133) that is converted from the coordinatesof the location (111). If a match is found, the location (111) isdetermined to be within the region (101); otherwise, the location (111)is determined to be outside of the region (101).

To improve the accuracy in the approximation of the region (101) andcomputational efficiency, the cells of a hierarchical grid system isused to approximate the region (101). FIGS. 5-7 illustrate ahierarchical grid system used to determine whether a location of amobile device is within the geographical boundary of a region accordingto one embodiment.

In FIG. 5, grids of different resolutions are used to identify a set ofcells to approximate the region (101). The grids has a predeterminedhierarchy, in which the grid lines of a high level grid aligns with someof the grid lines of a low level grid such that the cells of the lowlevel grid subdivide the cells of the high level grid. The grids ofdifferent resolutions have different cell sizes.

In general, a grid having a higher resolution and thus smaller cell sizecan approximate the region (101) in better precision, but uses morecells.

In one embodiment, the cells from the lower resolution grid is used inthe interior of the region (101) to reduce the number of cells used; andthe cells from the higher resolution grid is used near the boundary ofthe region (101) to improve precision in using the set of cells toapproximately represent the region (101).

For example, in one embodiment, the lowest resolution gird is applied toidentify a set of cells to approximate the region (101). The cells inthe lowest resolution grid that contain the boundary of the region (101)are split in accordance with the grid of the next resolution level toidentify cells in the grid of the next resolution level for improvedprecision in representing the region (101). The cell splitting processcan be repeated for further improved precision using a higher resolutiongrid.

FIG. 6 illustrates the use of cells from two levels of hierarchicalgrids to approximate the region (101).

After the set of cells used to approximate the region (101) areidentified (e.g., as illustrated FIG. 6), the system is configured todetermine whether the location (111) of the mobile device (109) iswithin the region (101) based on whether any of the set of cellsrepresenting the region contains the location (111), in a way asillustrated in FIG. 7.

For example, in one embodiment, each of the cells used in FIG. 7 torepresent a part of the region (101) is provided with a cell identifier.The coordinates of the location (111) is mapped to a cell identifier ata given resolution level. The system is configured to search in the setof cell identifiers of region (101) at the corresponding resolutionlevel to determine whether there is a match to the cell identifier asdetermined from the coordinates of the location (111). If a match incell identifier is found at any resolution level, the location (111) isdetermined to be within the region (101) represented by the set ofcells; otherwise, the location (111) is determined to be outside theboundary of the region (101).

In one embodiment of FIG. 1, a hierarchical grid system is used toapproximate the predefined regions (101, 103, . . . , 105, 106) withcells. Each of the cells is classified/identified as being in one ormore of the regions (101, 103, . . . , 105, 106). The database (181)stores the identifiers of the cells in association with the identifiesof the respective regions (101, 103, . . . , 105, 106); and the server(187) is configured to compute the identifiers of the cells of differentresolutions that contain the location (111) and determine if any of thecell identifiers stored in the database (181) in association with theidentifiers of the regions (101, 103, . . . , 105, 106) has the samecell identifier as the location (111). If a matching cell identifier isfound, the location (111) of the mobile device (109) is determined to bewith the respective region(s) (e.g., 101) associated with thecorresponding cell identifier; otherwise, the location (111) isdetermined to be outside all of the regions (101, 103, . . . , 105, 106)represented by the set of cell identifiers stored in the database (181).

FIGS. 8 and 9 show a top level grid and the identification of cellswithin the grid according to one embodiment.

In one embodiment, the location (111) of the mobile device (109) isdetermined to be on the surface of the earth in terms of the longitudeand latitude coordinates. In a coordinate system as illustrated in FIG.8, the longitude coordinates are configured to be within the range of−180 degrees to 180 degrees; and the latitude coordinates are configuredto be with the range of −90 degrees to 90 degrees.

In one embodiment, a hierarchical grid system on the surface of theearth is based on a regular grid in the longitude latitude spaceillustrated in FIG. 9.

In FIG. 9, the cells in the top level grid have a uniform size of 10degrees in longitude and 10 degrees in latitude. In FIG. 9, the cellsare identified by the row identifiers ranging from −9 to −1 and 1 to 9and column identifiers ranging from 1 to 36.

In FIG. 9, the row and column identifiers are configured in a way toavoid using zero as a row identifier or a column identifier.

In FIG. 9, the row identifier of 1 is assigned to the row of cellsbetween 0 to 10 degrees of latitude; the row identifier of 2 is assignedto the row of cells between 10 to 20 degrees of latitude; etc. The rowsof cells between 0 to −90 degrees of latitudes are assigned similar rowidentifiers with a negative sign. For example, the row identifier of −1is assigned to the row of cells between 0 to −10 degrees of latitude;the row identifier of −2 is assigned to the row of cells between −10 to−20 degrees of latitude; etc. As a result, the row identifier has a signand a single digit for the top level cells illustrated in FIG. 9; andthe single digit is not zero for any of the rows. Thus, for eachlocation that is inside a cell in the top level grid as illustrated inFIG. 9, the row identifier of the cell containing the location has thesame sign as the latitude coordinate of the location and the singledigit that equals to 1 plus the tens digit of the latitude coordinate ofthe location.

In FIG. 9, the column identifier of 1 is assigned to the column of cellshaving longitude coordinates between −180 to −170 degrees; the columnidentifier of 2 is assigned to the column of cells having longitudecoordinates between −170 to −160 degrees; etc. Thus, for each locationthat is inside a cell in the top level grid as illustrated in FIG. 9,the column identifier of the cell containing the location has no sign(e.g., the column identifier is always greater than zero) and one or twodigits that correspond to adding 18 to a number formed by using thehundreds digit of the longitude as the tens digit and the tens digit ofthe longitude as the ones digit.

The combination of the row identifier and the column identifier of acell uniquely identifies the cell within the top level grid asillustrated in FIG. 9. For example, the digits of the column identifiercan be appended to the row identifier to generate a signed number thatuniquely identifies the cell within the grid illustrated in FIG. 9. Fora given cell identifier, the row identifier and the column identifiercan be unambiguously deduced from the cell identifier itself, since therow identifier has a signal digit and a sign. The longitude and latitudecoordinates of the vertexes of the cell can be computed from the rowidentifier and the column identifier.

Although FIG. 9 illustrates a preferred way to code the row identifiersand the column identifiers based on the longitude and latitudecoordinates of the locations within the cells, alternative codingschemes can be used.

For example, the rows can be coded from 1 to 18 for latitudes from −90degrees to 90 degrees; and the columns can be coded from 10 to 45 forlongitudes from −180 degrees to 180 degrees. Thus, both the row andcolumn identifiers are positive integers, while the column identifiersalways have two digits.

For example, the rows can be coded from 11 to 28 for latitudes from −90degrees to 90 degrees; and the columns can be coded from 11 to 46 forlongitudes from −180 degrees to 180 degrees. Thus, both the row andcolumn identifiers are positive integers having two digits.

FIG. 10 shows an intermediate level grid and the identification of cellswithin the grid according to one embodiment. In FIG. 10, a given cell ata higher level grid (e.g., a cell in the top level grid as illustratedin FIG. 9) is subdivided into 10 rows and 10 columns. The coding of therows and columns correspond to the measurement directions of thelongitude and latitudes coordinates such that the corresponding digitsin the longitude and latitudes coordinates at a given precision levelcan be used directly as the row and column identifiers of the sub-cellswithin the cell at the higher level grid.

For example, when the cell that is being subdivided into the 10 rows and10 columns has a size of 10 degrees in longitude and 10 degrees inlatitude (e.g., as illustrated in FIG. 9), the row identifier and columnidentifier of the sub-cells correspond to the ones digit of the latitudeand longitude coordinates of the points within the respective sub-cells.

For example, when the cell that is being subdivided into the 10 rows and10 columns has a size of 1 degree in longitude and 1 degree in latitude,the row identifier and column identifier of the sub-cells correspond tothe one-tens digit of the latitude and longitude coordinates of thepoints within the respective sub-cells.

FIG. 11 shows the identification of cells within a grid having thefinest resolution in a grid hierarchy according to one embodiment. InFIG. 11, the row identifiers and column identifiers are padded by 1, incomparison with the row and column coding scheme illustrated in FIG. 10.

In one embodiment, an identifier cell for a given resolution includessufficient information to identify the corresponding cells in the higherlevel grid(s) that contains the cell. Thus, a cell identifier uniquelyidentifies a cell in the entire hierarchical grid without ambiguity.

FIG. 12 shows the method to determine whether a location of a mobiledevice is within the geographical boundary of a region according to oneembodiment.

In FIG. 12, the location (111) of the mobile device (109) is determinedin terms of the longitude coordinate (143) and the latitude coordinate(145).

For a given resolution level (147), the longitude coordinate (143) andthe latitude coordinate (145) are truncated to generate the columnidentifier (149) and the row identifier (151). Applying (155) theresolution level (147) includes truncating the longitude coordinate(143) and the latitude coordinate (145) to the corresponding digits ofprecision to generate the column identifier (149) and the row identifier(151). In one embodiment, the digits corresponding to the top level gridand the bottom level grid at the given resolution are adjusted accordingto FIGS. 9 and 11.

In FIG. 12, the column identifier (149) and the row identifier (151) arecombined to generate the cell identifier (153) of the location (111) ofthe mobile device at the given resolution level (147).

In one embodiment, the database (181) stores a set of cell identifiers(161, . . . , 163) that are associated with the region (101) defined bya predetermined boundary. The server (187) searches (157) the set ofcell identifiers (161, . . . , 163) to find a match with the cellidentifier (153). If a match is found, the location (111) of the mobiledevice (109) is determined to be within the boundary of the region(101).

In one embodiment, the database (181) stores a set of cell identifiers(e.g., 161, . . . , 163, 165, . . . ) associated with respectivedifferent regions (e.g., 101, 103, . . . ). When the cell identifier(153) of the location (111) of the mobile device (109) is found to bematching with a particular cell identifier (e.g., 163 or 165), theregion (e.g., 101 or 103) associated with the particular cell identifier(e.g., 163 or 165) is determined to be the region in which the mobiledevice (141) is located.

In one embodiment, when a cell contains the boundary of two regions(e.g., 101 and 103), the cell identifier of the cell can be associatedwith both regions (e.g., 101 and 103). The system may optionally furtherdetermine which region the cell is in based on the coordinates of thevertexes defining the boundary (or other parameters that define theboundary between the regions).

FIG. 13 illustrates an example of converting the coordinates of alocation to an identifier of a cell and converting the identifier of thecell to the coordinates of a vertex of the cell according to oneembodiment.

In FIG. 13, the location has a latitude coordinate of −51.12345678 and alongitude coordinate of −41.12345678. A resolution at the fifth digitafter the decimal point is applied to the coordinates to generate thetruncated coordinates (−41.12345, −51.12345). The decimal point isremoved to obtain the longitude digits −4112345 and the latitude digits−5112345. Since the length of the equator of the earth is about 40,075km, the cell size near the equator is about 1.11 meters at theresolution corresponding to the fifth digit.

In accordance with the scheme for the top level grid illustrated in FIG.9, the tens digit for the latitude coordinate is padded with one(without considering the sign of the latitude); and the hundreds digitand tens digit, including the sign, of the longitude coordinate ispadded with 18 to generate the row identifier −6 and the columnidentifier 14 for the top level grid.

In accordance with FIG. 10, the row identifiers and column identifiersof the sub-cells in the hierarchical grid correspond to the respectivelatitude digits and longitude digits (1, 1, 2, 3, 4).

In accordance with FIG. 11, the row identifiers and column identifiersof the sub-cells in the bottom hierarchy is padded with 1, if thelongitude and/or the latitude coordinates of the location is not exactlyon the grid lines of the resolution level (e.g., if the longitude orlatitude coordinate has nonzero digits after the fifth digit behind thedecimal point). One is not padded at the last digit when the longitudeand/or the latitude coordinates of the location is exactly on the gridlines of the resolution level (e.g., if the longitude or latitudecoordinate has no nonzero digits after the fifth digit behind thedecimal point). According to this padding scheme, in the northernhemisphere locations on the northern edge of a cell are included in thecell but not the locations on the southern edge. In the southernhemisphere, locations on the southern edge of a cell are included in thecell but not the locations on the northern edges. Locations on theeastern edge of a cell are included in the cell, but not the westernedge.

Thus, the location (−41.12345678, −51.12345678) has the row and columnidentifiers −6112346 and 14112346. The digits of the column identifierare appended to the digits of the row identifier to generate the cellidentifier −611234614112346.

In FIG. 13, the row and column identifiers can be recovered from thecell identifier. The number of digits in the cell identifier divided by2 provides the number of leading digits for the row identifier; and theremaining digits are for the column identifier. From the row identifierand column identifiers, the latitude digits and longitude digits can becomputed via subtraction of the respective padding. The truncatedcoordinates can be computed from the latitude digits and longitudedigits respectively, which can be used to determine the coordinates of avertex of the cell as (−41.12345, −51.12345). Based on the resolution ofthe cell being at 0.00001, the coordinates of other vertexes of the cellcan be determined as (−41.12346, −51.12345), (−41.12346, −51.12344),(−41.12345, −51.12344). The bounding box of the cell and the neighboringcells can also be easily identified based on the coordinates.

FIG. 13 illustrates a way to append the digits of the column identifierto the digits of the row identifier to generate the cell identifier.Alternatively, the row identifier and the column identifier can becombined in other ways that can be reversed to derive the row identifierand the column identifier from the cell identifier.

For example, when the top level column identifiers are mapped to therange 11 to 46 to have a fixed number of two digits for the top levelcolumn, the column identifier is 2411236. Since there is no ambiguity inthe number of digits used to represent the top level column, the toplevel column identifier (24) can be appended after the top level rowidentifier (−6), which is then appended with the row and columnidentifiers of the next level, and so on. Thus, a cell identifier of−6241111223366 can be generated, with the sign then the first threedigits representing the top level row and column, and two digits forsubsequent next level row and column to identifying the subdivisionwithin the higher level cell.

In some embodiments, the row and column identifiers of the bottom levelare not padded in a way illustrated in FIG. 11 to have different ways toaccount for the locations on grid lines at the lowest level resolution.

FIGS. 9-11 and 13 illustrate a grid hierarchy based on a decimalrepresentation of longitude and latitude coordinates. Alternatively, thegrid hierarchy can be constructed in accordance with longitude andlatitude coordinates expressed using other bases, such as binary,ternary, quintal, octal, duodecimal, etc. in a similar way.

Further, in some embodiments, the longitude and latitude coordinates maybe normalized (e.g., in the standardized data range between 0 to 1); andthe grids can be constructed in the space of the normalized longitudeand latitude coordinates.

The hierarchical grid can also be extended to a three-dimensional space.For example, a hierarchical grid can be constructed with regular gridsin the longitude, latitude, altitude space, or in a mapped or normalizedlongitude, latitude, and altitude space.

FIG. 14 shows a system configured to map a location of a mobile deviceto one or more identifications of regions according to one embodiment.In FIG. 14, the mobile device (109) determines the coordinates (171) ofits location (111) based on the wireless signals (179) to and/or from alocation determination system, such as the Global Positioning System(GPS).

The coordinates (171) are converted to a cell identifier (173) of a cellthat contains the location, e.g., in a way as illustrated in FIG. 12 or13.

In the database (181), a set of cell identifiers are stored inassociation with region identifiers (185), where each of the cellidentifiers is associated with one or more of the respective regionswhen the respective cell contains at least a portion of the one or moreof the respective regions.

In one embodiment, the set of cell identifiers are organized as a cellidentifier tree (183) to facilitate the search of a matching identifier.

For example, the cell identifier tree (183) can be constructed as aself-balancing tree for efficient searching of a cell identifiermatching the cell identifier (173) generated from the coordinates (171)of the mobile device (109).

In general, any methods to search for an identifier with a set ofpredetermined identifiers can be used to search for the matching cellidentifier (173).

From the association of the cells with the region identifiers (185) inthe database, the server (187) determines the identification (175) ofthe one or more defined regions that are at least partially in the cellidentified by the cell identifier (173). Thus, the location (111) of themobile device (109) is determined to be within the region(s) identifiedby the identification (175) of the defined region(s).

Similarly, after regions of different sizes and locations arerepresented via the cells in the hierarchical grid, the system can beconfigured to efficiently compute overlapping portions of regions viasearching for cells having the same identifications.

For example, to determine the approximate overlapping between regions,the percentage of overlapping, the square of overlap, etc., the systemis configured to count a number of overlapped cells to determine theoverlapping.

In one embodiment, a polygon or any other shape is approximated by a setof rectangular and/or square cell of different sizes in a suitablecoordinate system (e.g., in longitude latitude space). Each cell isrepresented by a single number as identifier. The identifiers of thecells used to approximate the polygon or shape can be organized as abinary tree, a self-balanced tree, a Red/Black Tree, or other structuresthat are known to provide logarithmic search time to improve thecomputation efficiency in determining whether a point is within thepolygon or shape.

For example, a polygon representing the boundary of United States ofAmerica USA on a map may include 2,000 vertexes. The Ray Castingalgorithm has O(n) complexity to calculate if a point is within thepolygon. When this polygon is approximated via a hierarchical gridsystem discussed above, the polygon can be represented 700 to 2,000,000cells in the longitude latitude space, depending on the requiredprecision. When the polygon is represented by 2,000,000 cells and theircorresponding identification numbers, searching a matching identifier atthe same precision via a binary tree gives log(2,000,000)=21 complexity,which is much less than 2,000. Thus, the present disclosure improves thecomputational efficiency of identifying a region in which a mobiledevice is located.

FIG. 16 shows a method of mapping a location of a mobile device to aregion according to one embodiment. For example, the method of FIG. 16can be implemented in the system of FIG. 1 and/or FIG. 14, using thegrid system illustrated FIGS. 2-8, and/or the grid system and cellidentifier system illustrated in FIGS. 8-13.

In FIG. 16, a computing apparatus is configured to: identify (221) a setof cells in a grid system that are within the predefined boundary of ageographic region; receive (223) a location (111) of a mobile device(109); convert (225) the location (111) to the identifier of a cell thatcontains the location; and search (227) identifiers of the set of cellsto determine if the cell identifier of the location (111) is in the set.If it is determined (228) that the cell identifier of the location (111)is in the set, the computing apparatus determines (229) that thelocation (111) of the mobile device (109) is in the geographic region.

In one embodiment, the computing apparatus includes at least one of: thedatabase (181) and the server (187).

In one embodiment, the database (181) is configured to store anidentifier of a geographical region (101) having a predefinedgeographical boundary defined by a set of vertexes (e.g., 123) or a setof other parameters, such as a center location and a radius.

The database (181) further stores a set of cell identifiers, each ofwhich identifies a cell that is determined to be within the predefinedgeographical boundary of the geographical region (101).

After the server (187) receives, from a mobile device (109), a location(111) of the mobile device (109), the server (187) converts a set ofcoordinates (143, 145) of the location (111) of the mobile device (109)to a cell identifier (153) of a cell that contains the location (111).In some embodiments, the mobile device (109) generates the cellidentifier (153) at a desired precision level to represent the location(111) of the mobile device (109).

The server (187) determines whether the location (111) of the mobiledevice (109) is within the geographical region (101) based on searchingthe set of cell identifiers to determine if the set has the cellidentifier (153) computed from the coordinates (143, 145) of thelocation (111) of the mobile device (109).

In one embodiment, to convert the set of coordinates (143, 145) of thelocation (143, 145) to the cell identifier (153), the server (187) (orthe mobile device (109)) generates two integers from longitude andlatitude coordinates of the location (111) of the mobile device (109)according to a precision level (e.g., resolution level (147), andcombine the two integers into the first cell identifier (153) withoutusing a floating point number computation.

In one embodiment, each cell using the in the system to approximate theregions and the locations is a rectangle/square area in a longitudelatitude space of locations on the earth. The size of the cell can beunambiguously determined from the cell identifier itself. Further, thelongitude and latitude coordinates of corners of the cell identified bythe cell identifier can be unambiguously determined from the cellidentifier itself.

In one embodiment, the set of cells identified by the set of cellidentifiers to approximate one or more regions (e.g., 101, 103, . . . ,105, . . . , 107) has a plurality of different cell sizes thatcorrespond to a plurality of predetermined cell resolution levels. Eachof the plurality of predetermined cell resolution levels corresponds toa predetermined precision level of longitudes and latitudes of locationson the earth. For example, each of the plurality of predetermined cellresolution levels corresponds to a precision to a predetermined digitafter the decimal point in longitude and latitude coordinates oflocations on the earth.

In one embodiment, a cell identifier itself includes sufficientinformation to determine the resolution level of the cell, thecoordinates of the vertexes of the cell, and the identifiers of theneighboring cells, etc.

In one embodiment, the database (181) stores data mapping each cellidentify in the set of cell identifiers to at least one regionidentifier, where the cell contains a least a part of each of theregions identified by the at least one region identifier. The server(187) is configured to search the set of cell identifiers to find a cellidentifier that matches with the cell identifier (153) computed from thelocation (141) and thus determine at least one region identifierassociated with the matching cell identifier.

For example, in one embodiment, the set of coordinates of the location(111) includes longitude (143) and latitude (145) of the location (111).To converting the coordinates (143, 145) to the cell identifier (153),the server (187) (or the mobile device (109)) selects digits from thelongitude (143) and the latitude (145) of the location (111) inaccordance with a cell resolution level (147) and combines the digitsselected from the longitude (143) and the latitude (145) of the location(111) into an integer representing the cell identifier (153) of thelocation (111).

As illustrated in FIG. 13, selecting the digits from the longitude andthe latitude includes: selecting digits from integer part of thelongitude and a first number of digits from the longitude after thedecimal point of the longitude to form an integer representation of thelongitude at the cell resolution level; and selecting digits frominteger part of the latitude and the same first number of digits fromthe latitude after the decimal point of the latitude to form an integerrepresentation of the longitude at the cell resolution level.

In one embodiment, to generate the column identifier and row identifierof the location (111), a predetermined number (e.g., one) is added to adigit of the integer representation of the latitude that corresponds tothe tens digit of the latitude; and a sign is provided to the integerrepresentation of the latitude according to the sign of the latitude.

In one embodiment, after providing a sign to the integer representationof the longitude according to the sign of the longitude, a predeterminednumber (e.g., eighteen) is added to digits of the integer representationof the longitude that corresponds to the hundreds digit and tens digitof the longitude, in view of the sign provided to the integerrepresentation of the longitude.

In one embodiment, when the latitude coordinate has a non-zero portionthat is discarded during the selection of the latitude digits for theinteger representation of the latitude, one is added to the ones digitof the integer representation of the latitude without considering thesign of the integer representation of the latitude. When the longitudecoordinate has a non-zero portion that is discarded during the selectionof the longitude digits for the integer representation, one is added tothe ones digit of the integer representation of the longitude withoutconsidering the sign of the integer representation of the longitude.

In one embodiment, after the server (187) receives data representing thepredefined geographical boundary of the geographical region, such as thecoordinates of the vertexes of a region having a polygon shape, thecoordinates of the center and the radius of a region having a circularshape, etc., the server (187) identify, in a hierarchy of cell grids,the set of cell identifiers that are determined to be within thepredefined geographical boundary.

In one embodiment, when the set of cells being searched having differentresolutions (cell sizes), the location (111) of the mobile device (109)is converted to a plurality of cell identifiers at the correspondingresolutions; and the server (187) is configured to search a match of anyof the cell identifiers at the corresponding resolutions computed fromthe location (111) of the mobile device (109).

For example, the identifiers of the cells of different sizes/resolutionsto represent the regions can be organized in a single tree; and theidentifiers of the location (111) of the mobile device (109) ofcorresponding sizes/resolutions can be searched concurrently or oneafter another to find a match.

For example, the identifiers of the cells of different sizes/resolutionsto represent the regions can be organized in separate trees according tocell sizes/resolutions; and the identifiers of the location (111) of themobile device (109) of corresponding sizes/resolutions can be searchedconcurrently or one after another in the respective trees forcorresponding sizes/resolutions.

In one embodiment, each grid in the hierarchy of cell grids correspondsto a rectangle/square grid in longitude latitude space of locations onthe earth with a predetermined resolution level that corresponds to aprecision level in a floating point decimal representation of longitudeand latitude coordinates.

The server (187) and/or the database (181) can be implemented as acomputer apparatus in the form of a data processing system illustratedin FIG. 15.

In one embodiment, the techniques disclosed above are used to maplocation histories of mobile devices into visitation histories of themobile devices to one or more pre-defined regions. The visitationpatterns of the mobile devices can be used to identify mobile deviceshaving similar behaviors and/or different behaviors in relation withlocations. For example, the differences in behaviors following an eventbetween mobile devices that are associated with the event and mobiledevices that are not associated with the event can be measured as aninfluence of the event, if the mobile devices have the same or similarbehaviors prior to the event.

For example, the mobile devices initially having similar behaviorprofiles may have different behaviors after some of the mobile devicesare provided with an advertisement and others are not. Thus, thedifference can be detected as a measurement of theinfluence/effectiveness of the advertisement.

FIG. 17 shows a method to detect differences in location patternsaccording to one embodiment.

In FIG. 17, the server (187) is configured to identify (241) a pair ofmobile devices, including a first mobile device associated with an eventand a second mobile device not associated with the event, to measure theinfluence of the event.

For the first mobile device, the server (187) is configured to: receive(243) locations of the first mobile device; convert (245) the locationsto one or more cell identifiers; search (247) for regions containing thecell identifiers; and generate (249) first data representing a locationpattern based on presence of the first mobile device in the regions.

For the second mobile device, the server (187) is similarly configuredto: receive (253) locations of the second mobile device; convert (255)the locations to one or more cell identifiers; search (257) for regionscontaining the cell identifiers; and generate (259) second datarepresenting a location pattern based on presence of the second mobiledevice in the regions.

The server (187) identifies (251) a difference of the first data and thesecond data as a measurement of an influence of the event. Preferably,the pair of mobile devices have similar attributes and/or locationpatterns prior to the event. Alternatively, the change in locationpatterns of the first mobile device before and after the event can becompared to the change in location patterns of the second mobile devicebefore and after the event to measure the influence of the event.

For example, the pair of mobile devices may be initially identified tohave similar profiles in location patterns and/or other attributes, suchas the demographic data of their users, the propensity scores of theirusers, etc. After the event of an advertisement being transmitted to theuser of the first mobile device but not the user of the second mobiledevice, the difference in the location patterns represents a measurementof the influence/effectiveness of the advertisement in changinglocation/visitation patterns.

Further, the differences in other attributes associated with the pair ofmobile devices can be determined as measurements of theinfluence/effectiveness of the advertisement with respect tocorresponding attributes. Examples of such attributes include theenrollment in a program or service, visitation to certain websites, foottraffic to a store, web traffic to a website, etc.

For example, the event may include an advertisement for a geographicalregion; and the method may be adapted to measure the effectiveness ofthe advertisement in changing a pattern of visitation to thegeographical region, as illustrated in FIG. 18.

FIG. 18 shows a method to detect differences in location patterns ofdifferent mobile devices visiting a predetermined region according toone embodiment.

In FIG. 18, the server (187) identifies (261) a pair of mobile devices,including a first mobile device associated with an event relevant to apredetermined region and a second mobile device not associated with theevent, to measure an influence of an event related to the region.

For the first mobile device, the server (187) is configured to: receive(263) locations of the first mobile device; convert (265) the locationsto one or more cell identifiers; determine (267) whether the locationsare in the region using the techniques discussed above based onsearching for matching cell identifiers; and generate (269) first datarepresenting a presence pattern of the first mobile device in theregion.

For the second mobile device, the server (187) is similarly configuredto: receive (273) locations of the second mobile device; convert (275)the locations to one or more cell identifiers; determine (277) whetherthe locations are in the region using the techniques discussed abovebased on searching for matching cell identifiers; and generate (279)second data representing a presence pattern of the second mobile devicein the region.

The server (187) identifies (271) a difference of the first data and thesecond data as a measurement of an influence of the event. Preferably,the pair of mobile devices have similar attributes and/or locationpatterns prior to the event. Alternatively, the change in locationpatterns of the first mobile device before and after the event can becompared to the change in location patterns of the second mobile devicebefore and after the event to measure the influence of the event.

In one embodiment, the measured influence is based on the differencebetween two groups of mobile devices, instead of the different betweentwo mobile devices, to account for the pattern variations in theindividual mobile devices.

FIG. 19 shows a method to measure the influence of an event based ondifferences in location patterns of mobile devices visiting apredetermined region according to one embodiment.

In FIG. 19, the server (187) is identifies (301) two groups of mobiledevices, including a first group associated with an event relevant to apredetermined region and a second group not associated with the event,to measure the influence of the event on groups of mobile devices.

The server (187) is configured to: receive (303) locations of mobiledevices in the two groups; convert (305) the locations to respectivecell identifiers; determine (307) whether the locations are in theregion based on whether the region has the respective cell identifiers;generate (309) first data representing a presence pattern of the mobiledevices in the first group in the region; and generate (311) second datarepresenting a presence pattern of the mobile devices in the secondgroup in the region.

The server (187) determines (313) a difference between the first dataand the second data as a measurement of the influence of the event.

In one embodiment, the server (187) determines the change of presencepattern of each mobile device, the average change of presence pattern inthe first group, the average change of presence pattern in the secondgroup, and then the difference between the average changes of presencepattern in the two groups for a measurement of the influence of theevent.

In some embodiments, the presence patterns of mobile devices aredetermined to select groups of mobile devices having similar behaviors,including the presence patterns.

FIG. 20 shows a method to identify mobile devices having similarpatterns of visiting a predetermined region according to one embodiment.The mobile devices having similar behaviors can be divided into twogroups to measure the influence of an event.

In FIG. 20, the server (187) is configured to: receive (321) locationsof mobile devices; convert (323) the locations to respective cellidentifiers; determine (325) whether the locations are in apredetermined region based on whether the region has the respective cellidentifiers; identify (327) presence patterns of the mobile devices inthe region; and identify (329) a subset of the mobile devices based atleast in part on similarity in presence patterns.

The server (187) divides (331) the subset of mobile devices into twogroups, including a first group for association with an event relevantto a predetermined region and a second group not associated with theevent. The method of FIG. 20 can then be used to measure the influenceof the event.

In some embodiments, an event may have influence on the visitationpatterns of a mobile device in visiting multiple regions.

FIG. 21 shows a method to identify mobile devices having similarpatterns of visiting predetermined regions according to one embodiment.

In FIG. 21, the server (187) is configured to: receive (341) locationsof mobile devices; convert (343) the locations to respective cellidentifiers; identify (345) predetermined regions in which the locationsare, based on whether the regions have the respective cell identifiers;identify (347) presence patterns of the mobile devices in the regions;identify (349) a subset of the mobile devices based at least in part onsimilarity in the presence patterns; and divide (351) the subset ofmobile devices into two groups, including a first group for associationwith an event relevant to a predetermined region and a second group notassociated with the event.

In one embodiment, the locations of a mobile device are received as afunction of time. Thus, the visitations of the mobile devices aredetermined as a function of time to determine presence patterns based atleast in part on time.

FIG. 22 shows a method to measure the influence of an event according toone embodiment.

In FIG. 22, the server (187) is configured to: identify (361) a set ofmobile devices based at least in part on similarity in associatedpatterns; divide (363) the set of mobile devices into two groups,including a first group for association with an event relevant to apredetermined region and a second group not associated with the event;determine (365) a difference in associated patterns between the mobiledevices in the first group and the mobile devices in the second group;and generate (367) a measurement of the influence of the event based onthe difference.

In one embodiment, when the set of mobile devices initially havingsimilar but not identical associated patterns prior to the event, theserver (187) is configured determine the change of associated patternsfor each mobile device before and after the event. The server (187) thendetermines the difference in the changes of associated patterns in thefirst and second groups to measure the influence of the event.

For example, the above discussed techniques can be used to measure theadvertising effectiveness. In various embodiments, attribute dataincluded in a first user profile may be used to select a second userprofile that is substantially similar to the first user profile. Thefirst user profile may include an indication of exposure to advertisingcontent data associated with a location and the second profile may notinclude such an indication. For example, a first user profile may beassociated with a first user that has seen an advertisement for alocation and the second user profile may be associated with a seconduser who has not seen the ad. In various embodiments, propensity scorematching and/or other approaches may be used to select a second userprofile. For example, a propensity score may be generated based on theattribute data in the first user profile (e.g. demographic data,behavioral data, etc.) and the propensity score may be compared topropensity scores generated for other user profiles to select a seconduser profile. The second user profile may, for example, be associatedwith a propensity score that matches (e.g., most closely matches) thepropensity score associated with the first user profile.

According to some embodiments, first behavior information (e.g., achange in number/frequency of visits to a location over a period priorto and over a period after seeing an ad related to the location) may bedetermined based at least in part on an association between the firstuser profile and a location associated with the advertising contentdata. Second behavior information may be determined based at least inpart on an association between the second user profile and the location.An advertising effectiveness value may be generated based at least inpart on the first behavior information and the second behaviorinformation.

In one embodiment, attribute data included in a first user profile maybe used to select a second user profile that is substantially similar tothe first user profile. In various embodiments, attribute data mayinclude, for example, demographic data, behavioral data, data fromthird-party sources, psycho-graphic data, location visit frequencypatterns, shopping cart spend data (e.g., including similar productsand/or categories of products), and/or any other data associated with auser. A first user profile may include a user profile for a user thathas been exposed to advertising content associated with a location(e.g., an advertisement to drive foot traffic to the location). In someembodiments, attribute data included in a first user profile may becompared to attribute data associated with one or more other userprofiles associated with users who have not been exposed to theadvertising content. And a user profile including attributes that aresubstantially similar to (e.g., matches) the attributes included in thefirst user profile may be selected. Various approaches may be used toidentify (e.g., select) matching user profiles including, for example,propensity score matching, statistical matching approaches, one-to-onematching, and/or any other any other matching technique.

In various embodiments, the first user profile may include a userprofile from an exposed/test group, and the second user profile mayinclude a user profile from a control group and/or general populationgroup. The first user profile and second user profile may be used totest (e.g., measure) the effectiveness and/or influence of advertisingcontent data associated with a location (e.g., an advertisement to driveusers to a retail location). The first user profile (e.g., theexposed/test group user profile) may include an indication that a userassociated with the first user profile has been exposed to advertisingcontent data associated with a location. And the second user profile(e.g., control group user profile, general population user profile) mayinclude an indication that a user associated with the second userprofile has not been exposed to the advertising content. In variousembodiments, to accurately measure the influence of the advertisingcontent data, the second user profile may be selected such that anyattributes, characteristics, biases, confounding variables, and/or otherfactors that may affect the outcome of the measurement are reducedand/or eliminated. In certain cases, any variables potentially affectingthe outcome of the measurement may be reduced by selecting a second userprofile that is substantially similar (e.g., as close as possible) tothe first user profile.

By way of example, a first user profile may include attribute dataincluding demographic data (e.g., data indicating that the user isfemale, 30-40 years old, resides in San Francisco, Calif., has ahousehold income of $100,000, etc.), behavioral data (e.g., the uservisits a coffee shop three times per week), third party data (e.g.,purchased a condo for $200,000 in 2006), psycho-graphic data (e.g.,leads a healthy lifestyle, likely to vote for a particular politicalparty, etc.), and other attribute data. Based on the attribute data, asecond user profile that matches (e.g., is substantially similar to) thefirst user profile may be selected. The second user profile may includesimilar (e.g., matching) attribute data including demographic data(e.g., user is female, 30-40 years old, residing in San Francisco,Calif., household income of $95,000, etc.), behavioral data (e.g.,visits the coffee shop four times per week), and/or other attributedata.

In one example matching approach, the attribute data from user profilesmay be used in a regression approach (e.g., logistic regression, linearregression, etc.) to generate a model (e.g., generalized linear model(GLM), logit model, discrete choice model, etc.). For example, a model(e.g., generalized linear model (GLM)) may represent a correlationbetween a dependent variable of whether or not a user has been exposedto advertising content and a set/vector of covariates includingattribute data included in the user profiles. The model (e.g.,generalized linear model (GLM)) may be used to generate propensityscores for each of the multiple profiles. In some embodiments, apropensity score associated with the first user profile (e.g.,associated with a user who has seen an ad) may be used toidentify/select a matching (e.g., most closely matching) second userprofile (e.g., associated with a user who has not seen the ad). Avariety of matching approaches including nearest neighbor, kernel, locallinear, caliper, and/or other matching techniques may be used to matchthe first and second user profiles based, for example, on propensityscores.

In one embodiment, first behavior information may be determined based atleast in part on an association between the first user profile and alocation associated with advertising content data. In variousembodiments, behavior information may include information associatedwith a user's presence at one or more locations. In some embodiments, afirst behavior information may include a number of instances, a numberof instances over a period of time, and/or a frequency/rate that a userassociated with the first user profile has been determined to be presentat the location (e.g., visited the location). For example, a user may bedetermined to be present at a location based on location data (e.g.,latitude/longitude and/or other location identifying information)received from a mobile device associated with the user. In certaincases, the location data may be received in connection with anadvertisement request, a WiFi login page, marketing opportunity within amobile application, entering a geo-fence, a deal and/or opportunityassociated with a mobile device, etc. In various embodiments, locationdata received from a user device may be mapped to one or more definedlocations. And based on a mapping of location data to a locationassociated with advertising content data, a user may be determined to bepresent at that location. When a user is determined to be present at alocation, a user profile associated with that user may be updated toinclude information (e.g., behavioral information) associated with theuser's presence at the location. For example, the user profile may beupdated to include the location, a time (e.g., time/day stamp) ofpresence, duration of presence (e.g., five minutes), and/or otherinformation related to the user's presence at the location. Thisinformation may be used to determine behavior information associatedwith the user profile and the location.

According to various embodiments, behavior information may include anumber of times that and/or frequency with which a user associated witha user profile has been present at a location prior to and/or afterbeing exposed to a digital advertisement. For example, a user associatedwith a first user profile may receive a digital advertisement includingadvertising content data associated with a location at certain time(e.g., a time (t0), a date, etc.). The time at which a user is exposedto advertising content data may include an advertising exposure time(e.g., time of exposure). In various embodiments, a user may have beenexposed to advertising content data multiple times and the advertisingexposure time may include the time of first exposure, time of lastexposure, an average/median time over a period of multiple exposures,and/or any other time.

In some embodiments, behavior information associated with a first userprofile may include a number of times a first user visited the locationover a period of time (e.g., one week, three days, etc.) prior toexposure to advertising content data (e.g., viewing an ad). The periodprior to exposure may include, for example, a look-back period. Thelook-back period may include any period of time (e.g., a predefinedperiod, arbitrary period, etc.). A number, frequency, and/or rate atwhich a user visits a location during the look-back period may include anatural visit frequency/rate. A natural visit rate may represent a rateat which a user visits a location in the absence of exposure toadvertising content (e.g., of the user's own volition, uninfluenced byadvertising content, etc.).

In various embodiments, behavior information associated with the firstuser profile may include a number of times the user visited the locationover a period of time after the time of exposure to the advertisingcontent data (e.g., viewing the ad). The period of time afteradvertising exposure may include a look-forward period, and thelook-forward period may be selected/determined in a manner similar tothe look-back period. In certain cases, the look-forward period,however, may be selected to be substantially different than thelook-back period. In another example, behavior information may include afrequency (e.g., one time per day, three times per week, etc.) at whichthe user visited the location during the look-forward period afterexposure to the advertising content.

In some embodiments, behavior information may include a differencebetween a natural visit rate (e.g., a number of times and/or frequencyat which a user was at the location during a period of time (e.g., alook-back period) prior to exposure to the advertising content data) anda number of times and/or frequency at which the user was at the locationduring a period of time after exposure (e.g., a look-forward period).The first behavior information may, for example, include value(s)quantifying an increase, decrease, and/or lack of change of the firstuser's behavior relative to the location (e.g., presence at thelocation) prior to and after seeing an advertisement. In variousembodiments, an increase in presence at a location after viewingadvertising content may indicate that the advertising content wassuccessful in influencing the behavior of the user.

In various embodiments, behavior information may be determined based onlocation data from multiple mobile devices. For example, a user may bepresent at a location on a first day as determined by locationinformation from a first device. After the first day, the user mayreplace the first device with a second device. Subsequently the user maybe determined to be present at the location based on location data fromthe second device. In this case, location information received from bothdevices may be included in a user profile for the user, and behaviorinformation may be determined based on location data from both devicesthat is included in the user profile.

In one embodiment, second behavior information may be determined basedat least in part on an association between the second user profile andthe location. In various embodiments, the second behavior informationmay include a number of instances, a number of instances over a periodof time, and/or a frequency that a user associated with the second userprofile (e.g., a control group profile) has been determined to bepresent at the location (e.g., visited the location).

In various embodiments, the second behavior information may include achange, if any, between the second user's visit frequency over a period(e.g., a look-back period) prior to a point in time as compared with thesecond user's visit frequency over a period (e.g., a look-forwardperiod) after the point in time. The point in time (e.g., a referencetime) may include, for example, the time at which the first user wasexposed to the advertising content, a time relative to the time at whichthe first user was exposed to the advertising content, an arbitrarytime, a time selected to ensure a proper comparison with the firstbehavior information, and/or another time.

In one embodiment, an advertising effectiveness value (e.g., a valuerepresenting advertising effectiveness, advertising effectivenessindicator) may be generated based at least in part on the first behaviorinformation and the second behavior information. In some embodiments, anadvertising effectiveness value may include number(s), value(s),percentage(s), metric(s) (e.g., a return on investment (ROI) metric, keyperformance indicator (KPI)), and/or any other data. The advertisingeffectiveness value may represent a change in number of visits (e.g.,increase/lift in foot traffic) to a location as a result of exposure tothe advertising content data.

In various embodiments, an advertising effectiveness value may becalculated/generated based on the first and second behavior information.In some embodiments, the advertising effectiveness value may begenerated based on a comparison between a change in behavior from a time(e.g., a first time, a series of times, etc.) a first user sees an adrelative to their natural visit rate and a change in behavior of asecond user who did not see the ad at the same time (e.g., an absolutesame time, relative same time, etc.). Stated another way, theadvertising effectiveness value may be generated based on a comparisonof the first behavior information associated with a first user who sawan ad related to a location and second behavior information associatedwith a second user who did not see the ad. As discussed above, the firstbehavior information may include a change in a first user's visitbehavior after exposure to advertising content relative to their naturalvisit rate. In other words, the first behavior information may becalculated based on a comparison (e.g., difference, change, etc.) of afirst user's visit frequency to a location over a period of time (e.g.,a look-back period) prior to exposure to advertising content related tothe location and the user's visit frequency over a period after exposure(e.g., a look-forward period) to the advertising content. A secondbehavior information may include a change in behavior of a second user,who was not exposed to advertising content, as measured by a comparisonof the second user's visit frequency to the location over a period(e.g., look-back period) prior to a certain time (e.g., the time whenthe first user saw the ad, a time relative to the time the first usersaw the ad, an arbitrary time, etc.) and the second user's visitfrequency over a period (e.g., look-forward period) after that time. Thecomparison of the first behavior information and second behaviorinformation may be used to generate an incremental lift (e.g.,advertising effectiveness value, which can be positive, negative, and/orzero) associated with the advertising content.

By way of example, first behavior information may indicate that a firstuser visited a coffee shop four times in the two weeks (e.g., alook-back period) prior to exposure to an ad for the coffee shop (e.g.,an ad for a free coffee at the shop displayed to the first user on theirmobile device). This visit rate over the look-back period (four times intwo weeks (i.e., two times per week)) may include a natural visit ratefor the first user. The first behavior information may also indicatethat the first user visited the coffee shop four times in the weekfollowing exposure to the advertisement (e.g., a look-forward period). Asecond user profile may be matched to the first user profile using thematching techniques discussed herein. The second user may be a user withsimilar attributes to the first user. Second behavior information mayindicate that the second user visited the coffee shop three times overthe two weeks (e.g., a look-back period) prior to a point in time (e.g.,the time the first user was exposed to the ad, a reference time, etc.)and two times in the week after that point in time. The advertisingeffectiveness value may be calculated based on the first behaviorinformation and second behavior information. In one example, theadvertising effectiveness value may include a comparison between achange in the first user's visit frequency prior to and after adexposure time (e.g., four visits per week during the look-forward periodversus two visits per week during the look-back period or achange/increase of two visits per week) and a change in the seconduser's visit frequency prior to and after the point in time (e.g., twotimes per week during the look-forward period and 1.5 times per weekduring the look-back period or a change of 0.5 visits per week).

In various embodiments, the process of generating advertisingeffectiveness values may be repeated for multiple pairs of users (e.g.,associated with a location). And the multiple advertising effectivenessvalues may be aggregated (e.g., summed up, added together) to generatean aggregate advertising effectiveness value as discussed in detailbelow. An aggregate advertising effectiveness value including one ormore advertising effectiveness values may include a location conversionindex (LCI). In various embodiments, a group of users may be selected todetermine an effectiveness/influence of advertising content (e.g., indriving users to a retail location). The group of users may, forexample, be related to the location in some way (e.g., each user mayhave visited the location over a period of time, the users may havesimilar demographic attributes, etc.). The group of users may be dividedinto subgroups including an exposed subgroup (e.g., test subgroup) ofusers that have been exposed to the advertising content data and controlsubgroup including users not exposed to the advertising content data.Using the techniques discussed herein user profiles from the exposedsubgroup may be paired to user profiles from the control subgroup and/ora general population subgroup. And advertising effectiveness values maybe generated for each pairing of users, and the advertisingeffectiveness values may be aggregated (e.g., summed up) to generate anaggregate advertising effectiveness value. In various embodiments, theprocess of generating advertising effectiveness values may be performediteratively across many different user profiles.

In some embodiments, the process of generating advertising effectivenessvalues may be repeated for multiple types of advertising content. Forexample, advertising effectiveness values may be generated for multipleversions of advertising content data.

In various embodiments, a user profile may include, for example,demographic data (e.g., household income, residence, value of home(s),occupation, work location, age, gender), behavioral data, data fromthird party data sources (e.g., property records, social network profileinformation, etc.), mobile device data (e.g., a list of applications ona device), psycho-graphic data, location visit frequency patterns,shopping cart spend data (e.g., including similar products and/orcategories of products), and/or any other data associated with a user.

In some embodiments, behavioral attributes may be derived, for example,from a user's past locations (e.g., location pattern(s)), prior actions,and/or other data. For example, a user (e.g., associated with userprofile) may have been determined to be at a location based on alocation data received, for example, along with a mobile advertisingrequest (e.g., from the user's mobile device). The location data may bemapped to a business, place of interest, zip+4 code, and/or otherlocation. The mapped location data may be used to update a locationpattern in the user's profile. The location patterns, behaviorattributes, and/or other location-related information may be included ina location graph in, for example, the user's profile.

In some embodiments, demographic, behavioral, and/or other attributesassociated with the business, place of interest, etc. to which a user'slocation has been mapped may be included in a user profile associatedwith that user. For example, a business (e.g., location) may beassociated with demographic, behavioral, and/or other attributes. And asa result of a user's detected presence at the business, behavioraland/or other attributes associated with the business may be attributedto the user (e.g., added to a user profile associated with the user). Incertain cases, attributes added to a user profile may be confirmed to becorrect or incorrect based on other information (e.g., attributesassociated with other locations the same user has visited, informationfrom third party data sources, a user's device, etc.).

In some embodiments, an advertising effectiveness platform/serviceresiding on one or more servers generates advertising effectivenessvalues (e.g., advertising effective index(es), location conversionindex(es)/values, etc.) based on information derived from one or moreuser profiles. The advertising effectiveness service may query, mineand/or otherwise process user profile information stored in the userprofile data store. For example, user profile information may beselected from the user profile data store and behavior information maybe determined based on the selected user profile information.Advertising effectiveness values (e.g., generated based on the behaviorinformation) may be stored in an advertising effectiveness data store.In various embodiments, an advertising provider may use the advertisingeffectiveness service to measure the effectiveness (e.g., influence,value, ROI, etc.) of an advertising campaign.

In one embodiment, an advertiser, advertisement provider, advertisementplatform, and/or other entity may seek to determine an effectiveness ofan advertising campaign associated with a retail location (e.g., anadvertisement associated with a retail location). A first user may beselected based on a determination that the first user has been servedadvertising content associated with the campaign, the first user hasvisited the location prior to being served advertising content, and/orother criteria. In various embodiments, attribute data associated with afirst user (e.g., included in a first user profile) may be used toselect a second user. For example, location attribute data associatedwith the first user may indicate that the first user is a female, age20-30, and employed at a technology firm. The location attribute datamay also indicate that the first user visited the retail location (e.g.,a fashion retailer) four times in the month prior to viewing anadvertisement for the retail location. This natural visit frequencyprior to being served the advertising content may include normal visits,unaided visits, and/or other types of visits to the retail location.Based on the first user's attribute data, a second user may be selected.In various embodiments, the second user may be selected usingattribute-based matching, propensity score matching, and/or othermatching approaches. The second user may, for example, include a usermost similar (e.g., in demographic, behavioral, and/or other attributes;propensity score; and/or other metrics) to the first user. The seconduser may be selected based on a determination that the second user hasnot been exposed to the advertising content associated with the retaillocation and/or any advertising content associated with the retaillocation. In this example, a second user who is a female, age 20-30,employed at a law firm and visits the retail location three times permonth may be selected. Whereas, another user who is a male, aged 40-50,employed as a doctor, and visits the retail location two times perquarter may not be selected as a similar user. The user may, however, beselected as a randomly-selected user as discussed below.

In various embodiments, first behavior information may be determined. Incertain cases, the first behavior information may represent a comparisonof a number of visits prior to and after the first user has been exposedto the advertising content (e.g., has viewed an ad, is presumed to haveviewed an) associated with the retail location. According to someembodiments, second behavior information may be determined. In certaincases, the second behavior information may represent a number of timesthe second user visits the retail location prior to and after a certainpoint in time (e.g., the time the first user was exposed to theadvertising content, another time, etc.). In various embodiments, anadvertising effectiveness value may be generated based on the firstbehavior information associated with the first user and the secondbehavior information associated with second user. In variousembodiments, the advertising effectiveness value may quantify/representthe influence of the advertising content data associated with thelocation.

According to some embodiments, an advertising effectiveness value may begenerated based on a comparison of behavior information associated withthe first user and behavior information associated with arandomly-selected user (e.g., a user from the general population). Invarious embodiments, a randomly-selected user may be selected based on adetermination that the user is associated with the location (e.g., hasvisited the location over a period of time). It may be determined, forexample, that the user has visited (360) the retail location; however,demographic data associated with user may not be similar to thedemographic data associated with the first user. In various embodiments,an advertising effectiveness value may be generated based the behaviorinformation associated with the first user and behavior informationassociated with the randomly-selected user using the approachesdiscussed herein. Generating an advertising effectiveness value based ona comparison of the behavior information associated with the first userand a randomly-selected user may provide additional insight into theeffectiveness/influence of an advertisement.

In one embodiment, it may be determined that a user profile includes anindication of exposure to advertising content data and/or engagementto/with advertising content data. For example, an indication of exposureto advertising content data may include a record indicating that adigital advertisement including advertising content data associated witha location has been presented to a user. The indication may begenerated, for example, when a digital advertisement is output to a useron a device (e.g., a mobile device, computer, smart television, wearablecomputer, etc.). An indication of engagement to/with advertising contentdata may include a record indicating that a user has engaged withadvertising content by, for example, clicking on an ad, expanding an ad,engaging with an via voice input, and/or other records. In variousembodiments, an indication of exposure/engagement may be associated withuser profile and not a specific device. For example, a device (e.g., ahome computer) on which the user was presented advertising content dataand/or interacted with advertising content may be different than adevice detected to be at a location of interest. In some embodiments, anindication of exposure/engagement may be generated when it is determinedthat a user has viewed and/or is likely to have viewed an advertisementpresented in a non-digital medium (e.g., a print ad, mailedadvertisement, etc.).

In one embodiment, the user profile may be selected based on thedetermination that the user associated with the profile has beenexposed/engaged (e.g., is presumed to have viewed) to and/or engagedwith the advertising content data. In various embodiments, a first userprofile (e.g., test user profile) may be selected as a test user profile(e.g., for comparison with a control user profile as discussed herein)based on the determination that the first user profile includes anindication of exposure/engagement to the advertising content data.

In one embodiment, a continuity factor associated with a user profilemay be determined. In various embodiments, continuity factors associatedwith user profiles may be used to select statistically significant userprofiles. A continuity factor may indicate whether and/or to what extenta user was an active user (e.g., active in the system) prior to the timeat which advertising content is served and/or after the advertisingcontent has been served. A continuity factor, in some embodiments, mayinclude a heart-beat indicator associated with the user. For example, ifa user is determined to have been an active user on three separate daysin the week prior to being served an advertisement for a location andthree separate days after viewing the advertisement, the continuityfactor for that user may be determined to be three. In variousembodiments, the period of time prior to ad exposure and after adexposure may be selected based on various factors associated with theadvertising effectiveness calculation. The periods of time may, forexample, be provided via user interface and/or other console from anadvertiser.

In various embodiments, a continuity factor for a user profile may begenerated based on location data from multiple mobile devices. Forexample, a user may be present at a location on a first day asdetermined by location information from a first device. After the firstday the user may replace the first device with a second device.Subsequently the user may be determined to be present at the locationbased on location data from the second device. In this case, locationinformation received from both devices may be included in a user profilefor the user, and a continuity factor may be generated from the locationdata from both devices.

In one embodiment, it may be determined whether a continuity factor isabove a threshold. In various embodiments, a threshold continuity factormay be set to, for example, one or any other value. A continuity factorgreater than or equal to a threshold (e.g., one) may indicate that auser has been an active user before and after being served advertisingcontent. This may indicate that the user profile is viable to be used inthe propensity score calculation. In some embodiments, a continuityfactor below a threshold (e.g., one) may indicate that the user was notpresent in the system prior to being served the advertisement. A userprofile associated with a continuity factor below a threshold (e.g.,one) may not be viable to be used in the propensity score calculationfor purposes of evaluating the influence/effectiveness of advertisingcontent data. In this case, the user may not be selected and the processmay end.

In one embodiment, a user profile associated with a continuity factorabove a threshold may be selected. In various embodiments, a userprofile associated with a continuity factor value above a threshold maybe selected as a test user profile (e.g., first user profile).

In one embodiment, propensity scores may be generated based on attributedata included in one or more user profiles. In some embodiments, apropensity score may represent a conditional probability of assignmentto a particular treatment (e.g., exposure to the advertising content)given a set (e.g., vector) of observed covariates (e.g., attribute dataincluding, for example, demographic attributes, behavioral attributes,psycho-graphic data, etc.). For example, a propensity score mayrepresent a conditional probability of exposure to advertising contentgiven a vector of attribute data (e.g., demographic data, behavioraldata, psycho-graphic data, location visit frequency patterns, shoppingcart spend data (e.g., including similar products and/or categories ofproducts)).

In various embodiments, a propensity score associated with a userprofile may be calculated by regressing the variable of whether or notthe user has been exposed to advertising content against the attributedata included in the user profile. Using regression and/or otherstatistical approaches a model (e.g., generalized linear model (GLM),discreet choice model, etc.) may be generated representing a correlationbetween a dependent variable of whether or not a user has been exposedto advertising content and a set/vector of covariates includingattribute data in the user profiles. In various embodiments, attributedata may be selected for inclusion in the set/vector of covariates toadjust for natural visit patterns, seasonal visit patterns, events,and/or other factors associated with the location of interest. The model(e.g., generalized linear model (GLM)) may be used to generatepropensity scores for each of the multiple profiles. In someembodiments, the propensity score calculation process mayaccount/compensate for natural visit patterns, seasonal visit patterns,events, and/or other factors associated with the location by virtue ofthe attribute data included in the propensity score calculation. Forexample, matching user profiles based on propensity score may reducebias resulting natural visit patterns, seasonal visit patterns, events,and/or other factors.

In one embodiment, a first propensity score associated with the firstuser profile (e.g., a user profile in an exposed group) may be comparedto one or more propensity scores each associated with a user profile ina control group (e.g., a group of user profiles for users not exposed tothe ad content). In various embodiments, a first propensity scoreassociated with the first user profile (e.g., a test group user profile)may be compared to one or more propensity scores to determine matching(e.g., closest/best matching) propensity scores.

In one embodiment, it may be determined whether a first propensity scorematches one or more propensity scores. In some embodiments, a firstpropensity score may be compared to one or more propensity scores todetermine a most-closely matching propensity score. In certainembodiments, nearest neighbor, kernel, local linear, caliper, and/orother matching techniques may be used to match the first propensityscore to one or more propensity scores. In various embodiments, thefirst propensity score may be iteratively compared to multiplepropensity scores to identify a most-closely matching propensity score.For example, a first propensity score (e.g., associated with a firstuser profile) may include a scalar value of 0.7, and this score may becompared to multiple propensity scores (e.g., 0.72, 0.65, 0.6, etc.)each associated with a user profile. Based on this example comparison,the propensity score of 0.72 may be selected as a most closely matchingpropensity score. In the event no propensity score is determined tomatch the first propensity score, the process may end.

In some embodiments, propensity scores may be matched based on athreshold and/or limit. For example, a first propensity score may matcha second propensity score if the difference between the two propensityscores is within a threshold. For example, a first propensity scoreassociated with a first user profile may include a scalar value of 0.35and a second propensity score may include a scalar value of 0.3 and athreshold difference may be defined as 0.1. Because this differencebetween the first propensity score (e.g., 0.35) and second propensityscore (e.g., 0.3) is less than the threshold (e.g., 0.1), the secondpropensity score may be determined to match (e.g., potentially match)the first propensity score.

In one embodiment, user profiles may be selected based on the matchingpropensity scores. In various embodiments, based on the propensity scorematching process, the first user profile (e.g., including an indicationof exposure to the advertising content) may be matched to a second userprofile, and this pair of profiles may selected. Once selected, anadvertising effectiveness value may be calculated for the pair of userprofiles.

In an embodiment of a process of calculating behavior information, afirst timeline depicts a first user's behavioral patterns relative to alocation (e.g., a retail location, restaurant, etc.) over a period oftime. Each observation of the user (e.g., point) on the timeline mayrepresent a point in time at which the first user was observed at thelocation. As depicted in the first timeline, the first user may, forexample, have been served advertising content (e.g., associated with thelocation) at an ad exposure time (e.g., time of ad exposure, t0, etc.).In some embodiments, a look-back period may include a period prior tothe ad exposure time. A look-forward period may include a period afterthe ad exposure time. In some embodiments, the look-forward period andlook-back period may include equal or different lengths/durations oftime.

In some embodiments, first behavior information (e.g., associated with auser profile) may include a comparison of a first user's natural visitrate and post-advertising exposure visit rate (e.g., after exposure tothe advertising content) to the location. A natural visit rate mayinclude a number/frequency of user visits to the location over thelook-back period. A post-exposure visit rate may include anumber/frequency of visits to the location over the look-forward periodafter exposure to the advertising content. The first behaviorinformation may include a difference (if any) between the first user'spost-exposure visit rate and the natural visit rate.

In various embodiments, a second timeline is shown depicting a seconduser's behavioral patterns relative to a same location over a period oftime. The second user in this case may not have been exposed toadvertising content related to the location. In some embodiments, alook-back period for the second user may include a period prior to apoint in time (e.g., a reference time). A look-forward period mayinclude a period after the point in time. In various embodiments, thepoint in time (e.g., reference time) may be equivalent to theadvertising exposure time (e.g., the same absolute time) at which thefirst user was exposed to the advertising content, another timedetermined based on the first and/or second user profile attributes, anarbitrary time, and/or any other time.

In some embodiments, the look-back period associated with the seconduser may be related to the look-back period associated with the firstuser. In one example, the two periods may span equivalent period(s) oftime, though not necessarily the exact same period(s). For example, thefirst look-back period may include a first week (e.g., the lastWednesday in December to the first Wednesday in January, etc.), and thesecond look-back period may include (e.g., the first Saturday inFebruary to the second Saturday in February). In another example, thefirst look-back period and second look-back period may span periods oftime of varying duration. In various embodiments, similar relations maybe exist between the first look-forward period and second look-forwardperiod.

In various embodiments, the look-back period, look-back period,look-forward period, look-forward period may be determined/selectedbased on input from a user of the advertising effectiveness platform,attributes associated with the first/second user profiles, and/or otherparameters. In some embodiments, the look-back periods, and/orlook-forward periods, may be selected to account/adjust for naturalvisit patterns, seasonal visit patterns, events (e.g., weather events, asale at the location, etc.) associated with the location, and/or otherfactors that may influence/affect/skew the calculation of theadvertising effectiveness value.

By way of example with reference to the first user timeline, a firstuser may be observed (e.g., via a mobile device) at a restaurant threetimes during the look-back period (e.g., as indicated by the threepoints on the timeline during the look-back period). The look-backperiod may include a one-week period prior to an ad exposure time ofJan. 1, 2014. The first user may have been shown advertising contentrelated to the restaurant at the advertising exposure time (e.g., Jan.1, 2014). And during the look-forward period including the two-weekperiod after Jan. 1, 2014, the first user may be observed at therestaurant eight times. In this case, the first behavior information mayinclude a difference between the first user's frequency of visits to thelocation during the look-back period—three times per week—and the firstuser's visit frequency during the look-forward period—four times perweek. The first behavior information may include, for example, anincrease of one visit per week, a 33.3% increase in visits per week,etc.

As depicted, for example, in the second user timeline, a second user maybe observed at the restaurant (e.g., the same restaurant) four timesduring a second look-back period—the one-week period prior to Feb. 1,2014. The second user may also be observed at the restaurant five timesduring a second look-forward period—the two weeks after Feb. 1, 2014. Inthis case, the second behavior information may include a differencebetween the second user's visit frequency to the location during thefirst look-back period—four visits per week—and the second user's visitfrequency to the location during the second look-forward period —sixvisits over two weeks. The second behavior information may include, forexample, a decrease of one visit of per week, a 25% decrease in visitsper week, etc. In this case, the change in visit behavior after thereference time is negative (e.g., indicating a decrease). In certaincases, this negative value may be assumed to be the result from randombehavioral patterns of the second user, and may be changed to zeroindicating no change in behavior.

According to some embodiments, an advertising effectiveness value may becalculated based on the first behavior information and second behaviorinformation. In this case the advertising effectiveness value mayinclude a comparison between the first behavior information—an increasein one visit per week by the first user—and the second behaviorinformation—a decrease of one visit per week by the second user. In thiscase, the advertising effectiveness value may include and incrementaldifference (e.g., incremental lift) of two visits per week. This valuemay indicate that exposure/interaction with the advertising contentresulted in an increase visit frequency of two visits per week.

In one embodiment, two or more advertising effectiveness values may begenerated. In various embodiments, a group of users including similarattributes may be selected to determine an effectiveness/influence ofadvertising content (e.g., in driving users to a retail location). Forexample, an advertiser associated with a quick service restaurant (QSR)chain may seek to quantify the value of an adverting campaign in drivingfoot traffic a QSR location. A group of user profiles identified asregular QSR patrons (e.g., known to visit the QSR location twice perweek) may be selected. Within this group an exposed subgroup (e.g.,exposed audience) of user profiles that include an indication ofexposure to the advertising content may be identified. And a non-exposedsubgroup of user profiles may be identified. Advertising effectivenessvalues may be generated using the techniques discussed herein. Forexample, user profiles from the exposed subgroup may be paired tosimilar user profiles from the non-exposed group, behavior informationmay be determined (e.g., numbers/frequencies of visits to the QSRlocation before and/or after advertisement exposure), and advertisingeffectiveness values may be generated based on the behavior information.

In one embodiment, aggregate effectiveness value(s) may be generated. Invarious embodiments, multiple advertising effectiveness values may besummed, aggregated, added together and/or otherwise combined to generatean aggregate advertising effectiveness value (e.g., a locationconversion index). In various embodiments, an aggregate effectivenessvalue may include an advertising effectiveness value that has beenupdated based on other advertising effectiveness values. For example,two advertising effectiveness values may be merged/combined to generatea single advertising effectiveness value.

In various embodiments, advertising effectiveness values associated withany number of user profiles may be aggregated to generate the aggregateadvertising effectiveness value. An aggregate advertising effectivenessvalue may represent an increase, decrease, and/or lack of change in anumber of visits to retail location as a result of advertising contentserved to a defined group of users over a period of time. Continuingwith the above example, the advertising effectiveness values generatedbased on the comparisons of the user profiles in the exposed subgroupand the users in the non-exposed subgroup of regular QSR patrons may beaggregated. For example, advertising effectiveness values may begenerated for each user in the exposed subgroup and these values may beaggregated to generate an aggregate advertising effectiveness valueacross the group of regular QSR patrons. In one example, the aggregateadvertising effectiveness value may, for example, indicate that theadvertising campaign resulted in an increase of two visits per week peruser who received the advertisement. In another example, the aggregateadvertising effectiveness value may indicate a 25% increase in foottraffic to the QSR location over a defined period of time (e.g., oneweek before ad exposure compared to one week after ad exposure).

In various embodiments, advertising effectiveness values generated basedon a comparison of user profiles exposed to advertising content andrandomly-selected user profiles (e.g., not exposed to the ad content)may be included in an aggregate effectiveness value. For example,advertising effectiveness values may be generated based on comparisonsof user profiles included in the exposed subgroup of male frequent QSRpatrons to randomly-selected user profiles (e.g., not necessarily malefrequent QSR patrons). These advertising effectiveness values may beadded to an aggregate advertising effectiveness value, but may, forexample, be given less weight in the aggregation.

In one embodiment, an aggregate advertising effectiveness value may beadjusted. In various embodiments, an aggregate advertising effectivenessvalue may be scaled, normalized, and/or otherwise adjusted. For example,advertising effectiveness value(s) may be scaled to a value within arange of values (e.g., 0 to 100), percentage(s), and/or other value(s).

In various embodiments, advertising effectiveness values may includeadjustments for natural visit patterns, seasonal visit patterns, events,and/or other factors as a result of the matching processes (e.g.,propensity score matching), look-back period determinations,look-forward period determinations, and/or other processes discussedherein. In some embodiments, however, an aggregate advertisingeffectiveness value (e.g., generated based on one or more advertisingeffectiveness values) may be adjusted (e.g., post-calculation) toaccount for natural visit patterns, seasonal visit patterns, events(e.g., current events, weather, etc.), and/or other factors affectingvisit rates to a location. For example, an aggregate advertisingeffectiveness value reflecting ad campaign-driven visits to a retaillocation may be reduced to account for an increase in natural visitsover the holiday season.

In one embodiment, a digital advertisement associated with a locationmay be generated. In various embodiments, a digital advertisement mayinclude a coupon, a banner advertisement, a pop-up advertisement,embedded advertisement, and/or other promotional content associated witha location (e.g., aimed at driving foot traffic to the location). Forexample, a digital advertisement may include a coupon for a 20% discounton the purchase of a cup of coffee at a coffee shop.

In one embodiment, advertising effectiveness value(s) may be used toselect users to receive the digital advertisement. In variousembodiments, advertising effectiveness values may be used to select atype of user that would be most receptive to (e.g., most likelyinfluenced by) the digital advertisement. Continuing with the example,an advertising effectiveness value may have been previously generatedindicating that a coupon for a free muffin at the coffee shop resultedin an increased visit frequency of one visit per month among males,between 20-30 years old, with a median salary of $50,000 per year.Another advertising effectiveness value may have been generatedindicating that a coupon for a 15% discount on purchase of coffeeresulted in an increased visit frequency of two visits per week amongmales, between 40-50 years old, who regularly attend sporting events.Based on these advertising effectiveness values, user profilesassociated with males, between 40-50 years, who are likely to attendsporting events may be selected to receive the digital advertisement.

In one embodiment, a digital advertisement may be provided to mobiledevice(s) associated with the selected user profiles. In variousembodiments, providing digital advertisement to users in a group knownto respond favorably to similar advertisement content may increase thereturn on investment of a mobile advertising campaign.

Further examples and details can be found in U.S. patent applicationSer. No. 14/295,067, filed Jun. 3, 2014 and entitled “MeasuringAdvertising Effectiveness”, the entire disclosures of which applicationis hereby incorporated herein by references.

Measure of Mobile Visits Lifts

In one embodiment, location graphs are used to organize location dataand attributes associated with mobile devices and predeterminedgeographical regions that may be visited by the mobile devices. Acomputing process is provided to propagate the attributes via thelocation graphs such that the attributes of the mobile devices and/orthe predetermined geographical regions can be inferred according to astatistical model in a computationally efficient way. The attributes areused to quantify the characteristics of mobile devices and identifysimilar mobile devices. The impact of an event/information to thevisitation patterns of the mobile devices is statistically measured asthe differences between mobile devices that have been exposed to theevent/information and similar mobile devices that have not been exposedto the event/information. Computing methods are provided to evaluate theimpact statistically and provide a measurement of the impact.

Some details of location graphs can be found in U.S. Pat. App. Pub. No.2014/0012806, entitled “Location Graph Based Derivation of Attributes”,the entire disclosure of which is hereby incorporated herein byreference. Further details can be found in the section below entitled“LOCATION GRAPH”.

Some details of the computation of the impact of an event/information tovisitation patterns of mobile devices can be found in U.S. Pat. App.Pub. No. 2015/0348095, entitled “Measuring Advertising Effectiveness”,U.S. Pat. No. 9,374,671, entitled “Systems and Methods to Track RegionsVisited by Mobile Devices and Detect Changes in Location Patterns”, andU.S. patent application Ser. No. 15/165,983, filed on May 26, 2016 andentitled “Systems and Methods to Track Regions Visited by Mobile Devicesand Detect Changes in Location Patterns Based on Integration of Datafrom Different Sources”, the entire disclosures of which patentdocuments are hereby incorporated herein by reference.

In general, the users having similar or same profiles, includingdemographic profile data, behavioral profile data, psycho-graphicprofile data, purchase profile data, and/or location profile data, etc.,can be grouped as similar users. The similar users can be organized intotwo groups. One of the two groups is provided with a predeterminedcontent (e.g., an advertisement, an announcement, a notice, a TVprogram, a direct mail advertisement, etc.); and the other of the twogroup is not provided with the content. The subsequent user behaviors,such as location patterns, web visitation, service subscription, retailstore visitation, etc., can be compared between the groups to identifythe influence of the content. In some embodiments, the changes in theuser behaviors of the same users before and after the time of thepresentation of the content are identified; and the changes are comparedbetween the groups to identify the influence of the content.

Location Graph

It is challenging to identify, in a computer system, neighboringlocations in a computational efficient way for a large set of locations.

At least one embodiment disclosed herein provides an efficient methodfor a computing system to identify neighboring relations among a set oflocations on the surface of the Earth. The method uses a grid system,such as hierarchical grid systems illustrated in FIGS. 2-11, or othergrid systems (e.g., Military Grid Reference System (MGRS)) to maplocations to cells that contain the respective locations and to identifyneighboring locations and/or candidates for neighboring locations basedon cells that contain the respective locations and the neighboringcells.

FIGS. 23-25 illustrate a system to organize location data via a gridsystem according to one embodiment.

FIG. 23 illustrates a set (401) of locations (e.g., 411-415, 421-427).One embodiment disclosed herein provides a computational efficient wayto identify the neighboring locations among the set (401) of locations(e.g., 411-415, 421-427) using a grid of cells as illustrated in FIG.24.

FIG. 24 illustrates an operation of mapping locations (e.g., 411-415,421-427) to the cells (e.g., 431-439) in a grid system. In FIG. 24, eachlocation (e.g., 411-415, 421-427) is mapped to a corresponding cell(e.g., 431-439) that contains the location.

For example, in response to a determination that the locations (411-415)are in the cell (431), a set of data is stored in a computing system tofacilitate the look up of the specific locations (411-415) are locatedwithin the cell (431). Thus, from the identifier of the cell (431), theset of locations that are located within the cell (431) can be looked upfrom the set of data stored in the computing system.

For example, the identifiers of the locations (411-415) can be stored inassociation with the identifier of the cell (431) in a lookup table suchthat the table can be queried using the identifier of the cell (131) toreturn the identifiers of the locations (411-415). For example, thecomputing device can be configured to store an array with cellidentifiers as the indices of the array, and the lists of theidentifiers of the locations (e.g., 411-415) contained within therespective cells as the values of the array for the correspondingindices. Other data storage techniques can also be used to facilitatethe look up the locations that have been determined to be within thecell (431).

Preferably, the mapping of a location (e.g., 411) to a cell (e.g., 431)is performed via the direct manipulation of the coordinates of thelocation (e.g., 411) (e.g., based on resolution of the grid), withoutusing a stored data table. For example, the mapping as illustrated inFIG. 13 can be used to convert the coordinates efficiently to theidentifier of a cell at a desired grid resolution without using floatingpoint number computations, when a hierarchical grid system as discussedin connection with FIGS. 8-13 is used. Since the coordinates of thelocation (411) can be efficiently converted to the identifier of thecell (431) that contains the location (411), it is not necessary tostore data for the look up of the particular cell (431) that containsthe location (411), when the grid reference system discussed above isused.

In one embodiment, for each respective location (e.g., 411) in the setof locations (e.g., 411-415, 421-427), the coordinates of the respectivelocation (e.g., 411) are combined to generate the identifier of the cell(e.g., 431) that contains the respective location (e.g., 411). A datapoint is then added to the cell-location data to allow the subsequentlook up of the respective location (e.g., 411) from the identifier ofthe cell (e.g., 431).

After the cell-location data is stored to facilitate the look up oflocations contained within respective cells, by using the identifier ofthe cell as the index or query criterion, neighboring locations (orcandidates of neighboring locations) of a location can be looked up fromthe identifier of the cell that contains the location and theidentifiers of the neighboring cells.

For example, to identify the neighboring locations of any locations(411-415) in the cell (431), the computing system computes theidentifiers of the neighboring/surrounding cells (e.g., 433-439) andthen looks up, using the identifiers of the neighboring cells (e.g.,433-439) in the cell-location data, the locations (e.g., 421-427) thatare contained within the neighboring cells. The collection of locationslooked up for being in the cell (431) and its neighboring cells(433-439) identifies the neighboring locations (or candidates ofneighboring locations).

Because the distances between a location (e.g., 411) within the cell(431) to any location (e.g., 413-415, 421-427) in the collection is lessthan a first threshold corresponding to a resolution of the grid (e.g.,twice the length of the diagonal line of a grid cell), the collection oflocations (e.g., 413-415, 421-427) are all within the first thresholdfrom the location (e.g., 411).

Further, any location that is within a second threshold away from thegiven location (e.g., 411) are necessarily within the cell (e.g., 431)and its neighboring cells (e.g., 433-439), where the second thresholdcorresponds to a resolution of the grid (e.g., the length or height ofthe grid cell). The collection of locations (e.g., 413-415, 421-427)looked up from the identifiers of the cells (e.g., 431-439) includes alllocations that are no more than the second threshold away from the givenlocation (e.g., 411).

In some instances, a predetermined distance threshold can be used toselect more precisely, from the collection of locations in the set ofneighboring cells (431-433), the neighboring locations of the givenlocation (411). For example, when the distance between a candidatelocation and the given location is no more than the distance threshold,the candidate location is selected as a neighboring location; otherwise,the candidate location is determined to be not a neighboring location.

The identification of the candidates based on the look up of locationsfrom neighboring cells (431-433) reduces the number of candidates andthus the computation load for the comparison to the distance threshold.Preferably, the resolution of the grid is selected according to thepredetermined distance threshold (e.g., in the same order as thethreshold) to minimize the candidates that are not neighboring cellsand/or minimize the neighboring cells that are to be looked up forcandidates.

Preferable, the cell identifiers of the neighboring/surrounding cells(e.g., 433-435) of a given cell (431) can be computed directly from theidentifier of the given cell (431) and/or the coordinates of a location(e.g., 411) inside the given cell (431) (e.g., as discussed inconnection with FIG. 13). Thus, there is no need to store data for thelook up of neighboring cells.

After the identification of the neighboring locations, the computersystem stores graph data representing the neighboring relations amongthe locations (e.g., 411-415, 421-427) in the set (401) of locations.The graph data includes nodes representing the locations (e.g., 411-415,421-427) and edges representing the neighboring relation between thelocations and/or the distance between the neighboring locations, asillustrated in FIG. 25.

For example, in FIG. 25, the locations (411-415) are found to be locatedwithin a threshold distance from each other and hence connected viaedges (441-445) in the graph of locations, where each of the locationsis represented as a node in the graph. For example, in FIG. 25, thedistance between locations (411 and 425) is less than the threshold andthus linked via an edge (447) in the location graph.

The graph data representing the neighboring relations among thelocations can be stored using various techniques, such as look up table,linked lists, arrays, etc. The graph data allows the look up of theneighboring locations in the set (401) for any given location (e.g.,411).

FIG. 26 illustrates a location data processing system to establish agraph of locations according to one embodiment.

In the system illustrated in FIG. 26, a set of algorithms (e.g., 451,453) are used to map locations to cells that contain the locationsrespectively, and map each cell to its neighboring cells. Through themapping of the locations to cells using an algorithm (e.g., 451),cell-location data (e.g., 455) can be established and stored tofacilitate the look up of locations contained within any cells. For eachrespective location, the cell contains the respective location and theneighboring cells are identified via the set of algorithms (e.g., 451,453) and then used in the cell-location data (e.g., 455) to look up thelocations that are contained within these cells. The looked up locationsare identified as the neighboring locations (or candidates for theneighboring locations) of the respective location; and location graphdata (457) can then be established and stored to facilitate the look upof neighboring locations of any location, in a way as illustrated inFIGS. 23-25.

In one embodiment, the algorithms (e.g., 451, 453) are based on a gridreference system (e.g., as illustrated in FIGS. 2-13). As illustrated inFIG. 13, the coordinates of a given location can be mapped to anidentifier of a cell at a desired resolution, by manipulations oflongitude digits and latitude digits to generate the column identifierand row identifier, which are further combined as a cell identifier.From the cell identifier, the coordinates of the vertices of the cellcan be identified. Further, the coordinates of the vertices, as well asthe identifiers, of the cells of the surrounding the cell can beidentified based on the cell resolution. The description of FIG. 13provides as further details.

Thus, the mapping relations from locations (e.g., 411, . . . , 415) tocells (e.g., 431) can be established based on the algorithm (451)applied to the coordinates of the locations (e.g., as illustrated inFIG. 13). Once a location (e.g., 411) is mapped to a cell (e.g., 431)that contains the location (e.g., 411), a portion of the cell-locationdata (e.g., 455) can be stored to allow the look up the location (e.g.,411) as part of the locations that are contained within the cell (e.g.,431). After all of the locations in a given set (e.g., 401) are mappedto the cells to store the relevant portions of the cell-location data(e.g., 455), the cell-location data (e.g., 455) can be used to look upall of the locations that are contained within any of the cells (e.g.,431).

Using the cell-location data (e.g., 455), the system then builds out thelocation graph data (e.g., 457) that connects any location (e.g., 411)to its neighboring locations (e.g., 413, . . . , 425). For example, forthe location (411), the system identifies the cell (431) that containthe location (411) and the surrounding cells (e.g., 433-439), byapplying the algorithms (e.g., 451, 453) to the coordinates of thelocation (411) and/or the identifier of the cell (431) in the gridreference system. The identifiers of the cell (431) and itsneighboring/surrounding cells (e.g., 433-439) are used in thecell-location data (e.g., 455) to look up all of the locations containedwithin the cell (431) and its neighboring/surrounding cells (e.g.,433-439) for the identification of the neighboring locations (e.g., 413,. . . , 425) of the location (411).

Thus, in the system illustrated in FIG. 26, no data is required to bepre-stored for the mapping from the locations (e.g., 411-415) to thecells (e.g., 431) that contains the locations (e.g., 411-415); and nodata is required to be pre-stored for the mapping from a cell (431) toits neighboring/surrounding cells (e.g., 433, . . . , 439).

For a given set (401) of locations (e.g., 411-415, 421-427) that arespecified by their coordinates, the algorithm (e.g., 451) converts theircoordinates to, in the grid reference system, the identifiers of thecells (e.g., 431) that contain the respective locations (e.g., 411-415).In response to the determination of the identifiers of the cells (e.g.,431) that contain the respective locations (e.g., 411-415), thecell-location data (e.g., 457) is stored to provide the mapping from thecells (e.g., 431) to the respective locations (411-415).

From the cell-location data that maps cells (e.g., 431) to respectivelocations (e.g., 411-415) that are contained within the respective cells(e.g., 431), the system identifies, for a given location (e.g., 411),the collection of neighboring locations (e.g., 413, . . . , 425) (orcandidates of neighboring locations) that are located with a cell (431)that contains the given location (e.g., 411) and theneighboring/surrounding cells (433, . . . , 439). Optionally, thedistances between the given location and the locations in the collectionof candidates are computed and compared to a threshold to identify theneighboring locations (e.g., 413, . . . , 425).

Once the neighboring locations (e.g., 413, . . . , 425) of the givenlocation (411) are identified, graph data (e.g., 457) is stored to mapthe location (411) to its neighboring locations (e.g., 413, . . . ,425). The process can be repeated for other locations (e.g., 415) toexpend the graph data to include the mapping of any location to itsneighboring locations.

FIG. 27 shows a method to generate a location graph according to oneembodiment. For example, the method of FIG. 27 can be applied in thesystem of FIG. 1 using a grid reference system illustrated in FIGS. 2-13to generate a location graph as illustrated in FIGS. 23-26.

In FIG. 27, a computing apparatus is configured to: receive (461) a set(401) of locations (e.g., 411-415, 421-427) each represented by itscoordinates; compute, (463) for each location (e.g., 411) using itscoordinates, the identifier of a cell (e.g., 431) that contains thelocation (e.g., 411) in a grid reference system (e.g., illustrated inFIGS. 8-13) and store cell-location data (e.g., 455) identifying, foreach respective cell identifier of a cell that contains one or more ofthe locations in the set (401), the locations that are contained withinthe cell (e.g., 431) having the respective cell identifier; compute(465), for each respective location (e.g., 411), the cell identifierfrom its coordinates and identifiers of neighboring cells to look up,from the cell-location data (e.g., 455), locations identified by thecomputed cell identifiers; and compute (467) the distances between therespective location (e.g., 431) and the looked up locations (e.g.,413-415, 421-429) and store location graph data (e.g., 457) linking therespective location (411) with the neighboring locations (e.g., 413, . .. , 425) edges identifying the computed distances.

In one embodiment, all of the looked up locations (e.g., 413-415,421-429) (other than the location (411) itself) are identified asneighboring locations. Alternatively, all of the looked up locations(e.g., 413-415, 421-429) (other than the location (411) itself) areidentified as candidates of neighboring locations; and the distancesbetween the location (411) and the candidates (e.g., 413-415, 421-429)are compared with a threshold to identify the neighboring locations(e.g., 413, . . . , 425) having distances to the location (411) that areno more than (or less than) the threshold.

The location graph data (457) can be used to propagate the attributes ofthe locations based on the proximity of the locations. For example, eachof the locations in the set (401) may represent a business or a point ofinterest. The computing device may store a profile for each of thelocation in the set (401), where the profile identifies one or moreknown attributes (e.g., keywords) that persons visiting the location arelikely to have. Due to the proximity of the locations, a person visitinga location is likely to visit a neighboring location; and thus aneighboring location is likely to have similar attributes. The locationgraph data (457) allows the system to propagate the profile attributesfrom locations to their neighboring locations based on the distances.For example, the likelihood of an attribute attachable to a personvisiting the location can be computed based on the weighted average ofthe likelihood of the attribute in the profile of the location and thelikelihoods of the attribute in the profiles of the neighboringlocations identified in the location graph data (457), where the weightsare based on the distances from the location to the neighboringlocations. The longer the distance, the smaller the weight. The weightedaverage can be used to update the likelihood of the attribute in theprofile of the location. The updating of the profile causes thepropagation of the profile attributes from locations to neighboringlocations.

The location graph can also be used to organize the locations trackedfor a mobile device (e.g., tracked using a location determinationdevice, such as a GPS receiver, of the mobile device).

In one embodiment, a computing device is configured (e.g., via softwareand/or hardware) to perform the operations to identify neighboringlocations among a set of locations and/or create and store the locationgraph data.

For example, the computing device of one embodiment includes: at leastone microprocessor; and memory storing instructions configured toinstruct the at least one microprocessor to: store, in the computingdevice, coordinates of a plurality of locations (e.g., 411-415, 421-427)on a surface of the Earth. In a grid reference system, the surface ofthe Earth is covered by a plurality of cells (e.g., as illustrated inFIGS. 8-11. For each respective location in the plurality of locations,the instructions, when executed, cause the at least one microprocessorto: combine coordinates of the respective location into an identifier ofa cell among the plurality of cells, where the cell contains therespective location on the surface of the Earth; and store, in thecomputing device, data associating the identifier of the cell and therespective location to facilitate a look up of the respective locationusing the identifier of the cell. For each respective location in theplurality of locations, the instructions, when executed, cause the atleast one microprocessor to: identify a plurality of neighboring cellsof the cell that contains the respective location on the surface of theEarth; look up, by the computing device, a subset of locations by usingthe identifier of the cell and the identifiers of the neighboring cellsin stored cell-location data that associates identifiers of respectivecells and locations contained within the respective cells; compute, bythe computing device, distances between the respective location andlocations in the subset; and store, in the computing device, graph datalinking the respective location to locations in the subset with edgesrepresenting the distances, wherein the plurality of locations arerepresented as nodes in the graph data.

For example, the coordinates of the respective location are combined toprovide the identifier of the cell according to a predetermined functionof the coordinates of the respective location, without using additionaldata stored in the computing device, and without using a floating pointnumber computation.

For example, the coordinates of the respective location are combinedvia: generating two integers from longitude and latitude coordinates ofthe respective location according to a precision level; and combiningthe two integers to provide the identifier of the cell.

For example, the coordinates of the respective location are combinedvia: selecting digits from the longitude and the latitude of thelocation in accordance with a cell resolution level; and combining thedigits selected from the longitude and the latitude of the location intoan integer representing the identifier of the cell.

For example, the plurality of neighboring cells are identified viacomputing identifiers of the neighboring cells from the identifier ofthe cell or the coordinates of the respective location; and thecomputing of the identifiers of the neighboring cells is based onpredetermined functions of the identifier of the cell, without usingadditional data stored in the computing device and without a floatingpoint number computation.

In one embodiment, a method implemented in the computing device,includes: storing, in the computing device, coordinates of a pluralityof locations on a surface of the Earth, wherein the surface of the Earthis covered by a plurality of cells. For each respective location in theplurality of locations, the method further includes: combining, by thecomputing device, coordinates of the respective location into anidentifier of a cell among the plurality of cells, wherein the cellcontains the respective location on the surface of the Earth; andstoring, in the computing device, data associating the identifier of thecell and the respective location to facilitate a look up of therespective location using the identifier of the cell. For eachrespective location in the plurality of locations, the method furtherincludes: identifying, by the computing device, a plurality ofneighboring cells of the cell that contains the respective location onthe surface of the Earth; looking up, by the computing device, a subsetof locations by using the identifier of the cell and the identifiers ofthe neighboring cells in stored cell-location data that associatesidentifiers of respective cells and locations contained within therespective cells; computing, by the computing device, distances betweenthe respective location and locations in the subset; and storing, in thecomputing device, graph data linking the respective location tolocations in the subset with edges representing the distances, whereinthe plurality of locations are represented as nodes in the graph data.

In the method of one embodiment, the coordinates of the respectivelocation are combined to provide the identifier of the cell withoutusing additional data stored in the computing device.

In the method of one embodiment, the coordinates of the respectivelocation are combined to provide the identifier of the cell according toa predetermined function of the coordinates of the respective location.

In the method of one embodiment, the combining of the coordinates of therespective location is performed by: generating two integers fromlongitude and latitude coordinates of the respective location accordingto a precision level; and combining the two integers to provide theidentifier of the cell. For example, the two integers are combined toform the identifier of the cell without using a floating point numbercomputation. In one embodiment, the cell is a rectangle area in alongitude latitude representation of the surface of the Earth.

In the method of one embodiment, the combining of the coordinates of therespective location is performed by: selecting digits from the longitudeand the latitude of the location in accordance with a cell resolutionlevel; and combining the digits selected from the longitude and thelatitude of the location into an integer representing the identifier ofthe cell.

In the method of one embodiment, the identifying of the plurality ofneighboring cells is performed by computing identifiers of theneighboring cells from the identifier of the cell. For example, thecomputing of the identifiers of the neighboring cells is based on theidentifier of the cell without using additional data stored in thecomputing device. For example, the computing of the identifiers of theneighboring cells is based on predetermined functions of the identifierof the cell. For example, the computing of the identifiers of theneighboring cells is based on the identifier of the cell without afloating point number computation.

In the method of one embodiment, the identifying of the plurality ofneighboring cells comprises computing identifiers of the neighboringcells from the coordinates of the respective location.

The method of one embodiment further includes: storing a set of keywordsin association each of the plurality of locations; and propagatingkeywords associated with the plurality of locations via the edges in thegraph data. For example, the propagating is performed based on weightscomputed based on distances represented by the edges in the graph data.

In one embodiment, a non-transitory computer storage medium storinginstructions configured to instruct a computing device to perform any ofthe methods discussed above.

FIG. 15 illustrates a data processing system according to oneembodiment. While FIG. 15 illustrates various components of a computersystem, it is not intended to represent any particular architecture ormanner of interconnecting the components. One embodiment may use othersystems that have fewer or more components than those shown in FIG. 15.

In FIG. 15, the data processing system (200) includes an inter-connect(201) (e.g., bus and system core logic), which interconnects one or moremicroprocessors (203) and memory (204). The microprocessor (203) iscoupled to cache memory (209) in the example of FIG. 15.

In one embodiment, the inter-connect (201) interconnects themicroprocessor(s) (203) and the memory (204) together and alsointerconnects them to input/output (I/O) device(s) (205) via I/Ocontroller(s) (207). I/O devices (205) may include a display deviceand/or peripheral devices, such as mice, keyboards, modems, networkinterfaces, printers, scanners, video cameras and other devices known inthe art. In one embodiment, when the data processing system is a serversystem, some of the I/O devices (205), such as touch screens, printers,scanners, mice, and/or keyboards, are optional.

In one embodiment, the inter-connect (201) includes one or more busesconnected to one another through various bridges, controllers and/oradapters. In one embodiment the I/O controllers (207) include a USB(Universal Serial Bus) adapter for controlling USB peripherals, and/oran IEEE-1394 bus adapter for controlling IEEE-1394 peripherals.

In one embodiment, the memory (204) includes one or more of: ROM (ReadOnly Memory), volatile RAM (Random Access Memory), and non-volatilememory, such as hard drive, flash memory, etc.

Volatile RAM is typically implemented as dynamic RAM (DRAM) whichrequires power continually in order to refresh or maintain the data inthe memory. Non-volatile memory is typically a magnetic hard drive, amagnetic optical drive, an optical drive (e.g., a DVD RAM), or othertype of memory system which maintains data even after power is removedfrom the system. The non-volatile memory may also be a random accessmemory.

The non-volatile memory can be a local device coupled directly to therest of the components in the data processing system. A non-volatilememory that is remote from the system, such as a network storage devicecoupled to the data processing system through a network interface suchas a modem or Ethernet interface, can also be used.

In this description, some functions and operations are described asbeing performed by or caused by software code to simplify description.However, such expressions are also used to specify that the functionsresult from execution of the code/instructions by a processor, such as amicroprocessor.

Alternatively, or in combination, the functions and operations asdescribed here can be implemented using special purpose circuitry, withor without software instructions, such as using Application-SpecificIntegrated Circuit (ASIC) or Field-Programmable Gate Array (FPGA).Embodiments can be implemented using hardwired circuitry withoutsoftware instructions, or in combination with software instructions.Thus, the techniques are limited neither to any specific combination ofhardware circuitry and software, nor to any particular source for theinstructions executed by the data processing system.

While one embodiment can be implemented in fully functioning computersand computer systems, various embodiments are capable of beingdistributed as a computing product in a variety of forms and are capableof being applied regardless of the particular type of machine orcomputer-readable media used to actually effect the distribution.

At least some aspects disclosed can be embodied, at least in part, insoftware. That is, the techniques may be carried out in a computersystem or other data processing system in response to its processor,such as a microprocessor, executing sequences of instructions containedin a memory, such as ROM, volatile RAM, non-volatile memory, cache or aremote storage device.

Routines executed to implement the embodiments may be implemented aspart of an operating system or a specific application, component,program, object, module or sequence of instructions referred to as“computer programs.” The computer programs typically include one or moreinstructions set at various times in various memory and storage devicesin a computer, and that, when read and executed by one or moreprocessors in a computer, cause the computer to perform operationsnecessary to execute elements involving the various aspects.

A machine readable medium can be used to store software and data whichwhen executed by a data processing system causes the system to performvarious methods. The executable software and data may be stored invarious places including for example ROM, volatile RAM, non-volatilememory and/or cache. Portions of this software and/or data may be storedin any one of these storage devices. Further, the data and instructionscan be obtained from centralized servers or peer to peer networks.Different portions of the data and instructions can be obtained fromdifferent centralized servers and/or peer to peer networks at differenttimes and in different communication sessions or in a same communicationsession. The data and instructions can be obtained in entirety prior tothe execution of the applications. Alternatively, portions of the dataand instructions can be obtained dynamically, just in time, when neededfor execution. Thus, it is not required that the data and instructionsbe on a machine readable medium in entirety at a particular instance oftime.

Examples of computer-readable media include but are not limited torecordable and non-recordable type media such as volatile andnon-volatile memory devices, read only memory (ROM), random accessmemory (RAM), flash memory devices, floppy and other removable disks,magnetic disk storage media, optical storage media (e.g., Compact DiskRead-Only Memory (CD ROM), Digital Versatile Disks (DVDs), etc.), amongothers. The computer-readable media may store the instructions.

The instructions may also be embodied in digital and analogcommunication links for electrical, optical, acoustical or other formsof propagated signals, such as carrier waves, infrared signals, digitalsignals, etc. However, propagated signals, such as carrier waves,infrared signals, digital signals, etc. are not tangible machinereadable medium and are not configured to store instructions.

In general, a machine readable medium includes any mechanism thatprovides (i.e., stores and/or transmits) information in a formaccessible by a machine (e.g., a computer, network device, personaldigital assistant, manufacturing tool, any device with a set of one ormore processors, etc.).

In various embodiments, hardwired circuitry may be used in combinationwith software instructions to implement the techniques. Thus, thetechniques are neither limited to any specific combination of hardwarecircuitry and software nor to any particular source for the instructionsexecuted by the data processing system.

The description and drawings are illustrative and are not to beconstrued as limiting. Numerous specific details are described toprovide a thorough understanding. However, in certain instances, wellknown or conventional details are not described in order to avoidobscuring the description. References to one or an embodiment in thepresent disclosure are not necessarily references to the sameembodiment; and, such references mean at least one.

The use of headings herein is merely provided for ease of reference, andshall not be interpreted in any way to limit this disclosure or thefollowing claims.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,and are not necessarily all referring to separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, variousfeatures are described which may be exhibited by one embodiment and notby others. Similarly, various requirements are described which may berequirements for one embodiment but not other embodiments. Unlessexcluded by explicit description and/or apparent incompatibility, anycombination of various features described in this description is alsoincluded here. For example, the features described above in connectionwith “in one embodiment” or “in some embodiments” can be all optionallyincluded in one implementation, except where the dependency of certainfeatures on other features, as apparent from the description, may limitthe options of excluding selected features from the implementation, andincompatibility of certain features with other features, as apparentfrom the description, may limit the options of including selectedfeatures together in the implementation.

In the foregoing specification, the disclosure has been described withreference to specific exemplary embodiments thereof. It will be evidentthat various modifications may be made thereto without departing fromthe broader spirit and scope as set forth in the following claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative sense rather than a restrictive sense.

What is claimed is:
 1. A computing device, comprising: at least onemicroprocessor; and memory storing instructions configured to instructthe at least one microprocessor to: store, in the computing device,coordinates of a plurality of locations on a surface of the Earth,wherein the surface of the Earth is covered by a plurality of cells; foreach respective location in the plurality of locations, combine, by thecomputing device, coordinates of the respective location into anidentifier of a cell among the plurality of cells, wherein the cellcontains the respective location on the surface of the Earth; and store,in the computing device, data associating the identifier of the cell andthe respective location to facilitate a look up of the respectivelocation using the identifier of the cell; and for the respectivelocation in the plurality of locations, identify, by the computingdevice, a plurality of neighboring cells of the cell that contains therespective location on the surface of the Earth; look up, by thecomputing device, a subset of locations by using the identifier of thecell and the identifiers of the neighboring cells in storedcell-location data that associates identifiers of respective cells andlocations contained within the respective cells; compute, by thecomputing device, distances between the respective location andlocations in the subset; generate, by the computing device, graph datalinking the respective location to locations in the subset with edgesrepresenting the distances, wherein when a distance between locations isless than a threshold distance the locations are linked via an edge inthe graph data; store, in the computing device, the generated graph datalinking the respective location to locations in the subset with theedges representing the distances that are each less than the thresholddistance, wherein the plurality of locations are represented as nodes inthe graph data; store, in the computing device, a set of keywords inassociation with the respective location; and propagate, by thecomputing device, the set of keywords via the edges to locations in thesubset, wherein the propagating is caused by updating a profile of agiven location in the subset based on a weighted average of a likelihoodof a keyword in the profile of the given location and likelihoods of thekeyword in respective profiles of locations identified in the generatedgraph data as neighboring the given location.
 2. The computing device ofclaim 1, wherein the coordinates of the respective location are combinedto provide the identifier of the cell according to a predeterminedfunction of the coordinates of the respective location, without usingadditional data stored in the computing device, and without using afloating point number computation.
 3. The computing device of claim 2,wherein the coordinates of the respective location are combined via:generating two integers from longitude and latitude coordinates of therespective location according to a precision level; and combining thetwo integers to provide the identifier of the cell.
 4. The computingdevice of claim 1, wherein the coordinates of the respective locationare combined via: selecting digits from the longitude and the latitudeof the location in accordance with a cell resolution level; andcombining the digits selected from the longitude and the latitude of thelocation into an integer representing the identifier of the cell.
 5. Thecomputing device of claim 1, wherein the plurality of neighboring cellsare identified via computing identifiers of the neighboring cells fromthe identifier of the cell or the coordinates of the respectivelocation; and the computing of the identifiers of the neighboring cellsis based on predetermined functions of the identifier of the cell,without using additional data stored in the computing device and withouta floating point number computation.
 6. A method implemented in acomputing device, the method comprising: storing, in the computingdevice, coordinates of a plurality of locations on a surface of theEarth, wherein the surface of the Earth is covered by a plurality ofcells; for each respective location in the plurality of locations,combining, by the computing device, coordinates of the respectivelocation into an identifier of a cell among the plurality of cells,wherein the cell contains the respective location on the surface of theEarth; and storing, in the computing device, data associating theidentifier of the cell and the respective location to facilitate a lookup of the respective location using the identifier of the cell; and forthe respective location in the plurality of locations, identifying, bythe computing device, a plurality of neighboring cells of the cell thatcontains the respective location on the surface of the Earth; lookingup, by the computing device, a subset of locations by using theidentifier of the cell and the identifiers of the neighboring cells instored cell-location data that associates identifiers of respectivecells and locations contained within the respective cells; computing, bythe computing device, distances between the respective location andlocations in the subset; generating, by the computing device, graph datalinking the respective location to locations in the subset with edgesrepresenting the distances, wherein when a distance between locations isless than a threshold distance the locations are linked via an edge inthe graph data; storing, in the computing device, the generated graphdata linking the respective location to locations in the subset with theedges representing the distances that are each less than the thresholddistance, wherein the plurality of locations are represented as nodes inthe graph data; storing, in the computing device, a set of keywords inassociation with the respective location; and propagating, by thecomputing device, the set of keywords via the edges to locations in thesubset, wherein the propagating is caused by updating a profile of agiven location in the subset based on a weighted average of a likelihoodof a keyword in the profile of the given location and likelihoods of thekeyword in respective profiles of locations identified in the generatedgraph data as neighboring the given location.
 7. The method of claim 6,wherein the coordinates of the respective location are combined toprovide the identifier of the cell without using additional data storedin the computing device.
 8. The method of claim 6, wherein thecoordinates of the respective location are combined to provide theidentifier of the cell according to a predetermined function of thecoordinates of the respective location.
 9. The method of claim 6,wherein the combining of the coordinates of the respective locationcomprises: generating two integers from longitude and latitudecoordinates of the respective location according to a precision level;and combining the two integers to provide the identifier of the cell.10. The method of claim 9, wherein the two integers are combined to formthe identifier of the cell without using a floating point numbercomputation.
 11. The method of claim 10, wherein the cell is a rectanglearea in a longitude latitude representation of the surface of the Earth.12. The method of claim 6, wherein the combining of the coordinates ofthe respective location comprises: selecting digits from the longitudeand the latitude of the location in accordance with a cell resolutionlevel; and combining the digits selected from the longitude and thelatitude of the location into an integer representing the identifier ofthe cell.
 13. The method of claim 6, wherein the identifying of theplurality of neighboring cells comprises computing identifiers of theneighboring cells from the identifier of the cell.
 14. The method ofclaim 13, wherein the computing of the identifiers of the neighboringcells is based on the identifier of the cell without using additionaldata stored in the computing device.
 15. The method of claim 13, whereinthe computing of the identifiers of the neighboring cells is based onpredetermined functions of the identifier of the cell.
 16. The method ofclaim 13, wherein the computing of the identifiers of the neighboringcells is based on the identifier of the cell without a floating pointnumber computation.
 17. The method of claim 6, wherein the identifyingof the plurality of neighboring cells comprises computing identifiers ofthe neighboring cells from the coordinates of the respective location.18. The method of claim 6, wherein the propagating is based on weightscomputed based on distances represented by the edges in the graph data.19. A non-transitory computer storage medium storing instructionsconfigured to instruct a computing device to perform a method, themethod comprising: storing, in the computing device, coordinates of aplurality of locations on a surface of the Earth, wherein the surface ofthe Earth is covered by a plurality of cells; for each respectivelocation in the plurality of locations, combining, by the computingdevice, coordinates of the respective location into an identifier of acell among the plurality of cells, wherein the cell contains therespective location on the surface of the Earth; and storing, in thecomputing device, data associating the identifier of the cell and therespective location to facilitate a look up of the respective locationusing the identifier of the cell; and for the respective location in theplurality of locations, identifying, by the computing device, aplurality of neighboring cells of the cell that contains the respectivelocation on the surface of the Earth; looking up, by the computingdevice, a subset of locations by using the identifier of the cell andthe identifiers of the neighboring cells in stored cell-location datathat associates identifiers of respective cells and locations containedwithin the respective cells; computing, by the computing device,distances between the respective location and locations in the subset;generating, by the computing device, graph data linking the respectivelocation to locations in the subset with edges representing thedistances, wherein when a distance between locations is less than athreshold distance the locations are linked via an edge in the graphdata; storing, in the computing device, the generated graph data linkingthe respective location to locations in the subset with the edgesrepresenting the distances that are each less than the thresholddistance, wherein the plurality of locations are represented as nodes inthe graph data; storing, in the computing device, a set of keywords inassociation with the respective location; and propagating, by thecomputing device, the set of keywords via the edges to locations in thesubset, wherein the propagating is caused by updating a profile of agiven location in the subset based on a weighted average of a likelihoodof a keyword in the profile of the given location and likelihoods of thekeyword in respective profiles of locations identified in the generatedgraph data as neighboring the given location.